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Saadi, Wafa, Fatima Zohra Laallam, Messaoud Mezati, Dikra Louiza Youmbai i Nour Elhouda Messaoudi. "Enhancing emotion detection on Twitter: an ensemble clustering approach utilizing emojis and keywords across multilingual datasets". STUDIES IN ENGINEERING AND EXACT SCIENCES 5, nr 2 (13.11.2024): e10548. http://dx.doi.org/10.54021/seesv5n2-522.
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Social media has become a vital element of everyday life, shaping domains like business, politics, and personal interactions. Emotions play a critical role in these areas, necessitating accurate detection and interpretation, especially on platforms like Twitter (X), which feature short texts, various data formats (such as words, Emojis, and numbers), and multilingual content, including dialects. This study explores the importance of Emojis and keywords in positively interpreting emotions on Twitter (X). It uses ensemble-clustering techniques, combining different clustering algorithms like KMeans with various methods for a detailed analysis of emotional subtleties in social media discourse. By merging the semantic meanings of Emojis and keywords, a novel clustering ensemble algorithm is proposed to improve emotion detection accuracy. The approach is tested on two datasets: English and Arabic dataset, using the Ekman model, which classifies emotions into six basic categories (joy, sadness, anger, disgust, surprise, and fear). The findings from this integrated method show greater accuracy and precision compared to individual methods, providing valuable insights into public sentiments, enhancing customer satisfaction analysis, and improving social media monitoring tools.
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Częstochowska, Justyna, Kristina Gligorić, Maxime Peyrard, Yann Mentha, Michał Bień, Andrea Grütter, Anita Auer, Aris Xanthos i Robert West. "On the Context-Free Ambiguity of Emoji". Proceedings of the International AAAI Conference on Web and Social Media 16 (31.05.2022): 1388–92. http://dx.doi.org/10.1609/icwsm.v16i1.19393.
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Due to their pictographic nature, emojis come with baked-in, grounded semantics. Although this makes emojis promising candidates for new forms of more accessible communication, it is still unknown to what degree humans agree on the inherent meaning of emojis when encountering them outside of concrete textual contexts. To bridge this gap, we collected a crowdsourced dataset (made publicly available) of one-word descriptions for 1,289 emojis presented to participants with no surrounding text. The emojis and their interpretations were then examined for ambiguity. We find that, with 30 annotations per emoji, 16 emojis (1.2%) are completely unambiguous, whereas 55 emojis (4.3%) are so ambiguous that the variation in their descriptions is as high as that in randomly chosen descriptions. Most emojis lie between these two extremes. Furthermore, investigating the ambiguity of different types of emojis, we find that emojis representing symbols from established, yet not cross-culturally familiar code books (e.g., zodiac signs, Chinese characters) are most ambiguous. We conclude by discussing design implications.
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Arjun Kuruva i Dr. C. Nagaraju. "A Robust Hybrid Model for Text and Emoji Sentiment Analysis: Leveraging BERT and Pre-trained Emoji Embeddings". Bioscan 20, nr 1 (24.01.2025): 186–91. https://doi.org/10.63001/tbs.2025.v20.i01.pp186-191.
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Sentiment analysis, a critical subfield of natural language processing, is widely employed to decipher the emotions andopinions expressed in textual data. With the growing prevalence of emojis in digital communication, understanding theircontribution alongside textual information has become paramount for comprehensive sentiment classification. This paperproposes a novel hybrid deep learning model for sentiment analysis that effectively integrates advanced feature fusion andattention mechanisms to address the challenges of analyzing multimodal data. By combining BERT-based textual embeddingsand pre-trained emoji embeddings, the model captures nuanced semantic and emotional information. A self-attentionmechanism further enhances the representation by identifying long-range dependencies and contextual relationships betweentext and emojis. The model was evaluated on the Sentiment140 dataset, achieving state-of-the-art performance with anaccuracy of 91.7%, an F1-score of 93.6%, and an AUC of 96.4%, outperforming existing models such as BERT-LSTM andRoBERTa-GRU. This superior performance demonstrates the effectiveness of multimodal fusion in sentiment classification,particularly for social media data where emojis play a significant role in emotional expression. The proposed architecture alsoshows strong generalizability, offering robust performance across diverse datasets. While computational complexity is a notedchallenge, future research could explore optimization techniques to improve efficiency without compromising accuracy. Thiswork highlights the potential of hybrid models to advance sentiment analysis by bridging the gap between textual and visual-emotional communication, setting a foundation for more comprehensive multimodal understanding in natural languageprocessing tasks.
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Nakonechnyi, O. G., O. A. Kapustian, Iu M. Shevchuk, M. V. Loseva i O. Yu Kosukha. "A intellectual system of analysis of reactions to news based on data from Telegram channels". Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, nr 3 (2022): 55–61. http://dx.doi.org/10.17721/1812-5409.2022/3.7.
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This paper describes the system of intellectual analysis and prediction of reactions to the news based on data from Telegram channels In particular, the features of collecting and pre-processing datasets for the intelligence systems, the methodology of thematic analysis of the received data, and the model used to obtain predictions of reactions to Telegram messages depending on their text are described We show the work of this system in the example of the Ukrainian news Telegram channel The results are estimations of probability of emojis for the news from the testing dataset Also, we give F-measures for our approaches to precise input data and models.
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Peng, Jiao, Yue He, Yongjuan Chang, Yanyan Lu, Pengfei Zhang, Zhonghong Ou i Qingzhi Yu. "A Social Media Dataset and H-GNN-Based Contrastive Learning Scheme for Multimodal Sentiment Analysis". Applied Sciences 15, nr 2 (10.01.2025): 636. https://doi.org/10.3390/app15020636.
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Multimodal sentiment analysis faces a number of challenges, including modality missing, modality heterogeneity gap, incomplete datasets, etc. Previous studies usually adopt schemes like meta-learning or multi-layer structures. Nevertheless, these methods lack interpretability for the interaction between modalities. In this paper, we constructed a new dataset, SM-MSD, for sentiment analysis in social media (SAS) that differs significantly from conventional corpora, comprising 10K instances of diverse data from Twitter, encompassing text, emoticons, emojis, and text embedded in images. This dataset aims to reflect authentic social scenarios and various emotional expressions, and provides a meaningful and challenging evaluation benchmark for multimodal sentiment analysis in specific contexts. Furthermore, we propose a multi-task framework based on heterogeneous graph neural networks (H-GNNs) and contrastive learning. For the first time, heterogeneous graph neural networks are applied to multimodal sentiment analysis tasks. In the case of additional labeling data, it guides the emotion prediction of the missing mode. We conduct extensive experiments on multiple datasets to verify the effectiveness of the proposed scheme. Experimental results demonstrate that our proposed scheme surpasses state-of-the-art methods by 1.7% and 0 in accuracy and 1.54% and 4.9% in F1-score on the MOSI and MOSEI datasets, respectively, and exhibits robustness to modality missing scenarios.
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Hauthal, Eva, Alexander Dunkel i Dirk Burghardt. "Emojis as Contextual Indicants in Location-Based Social Media Posts". ISPRS International Journal of Geo-Information 10, nr 6 (12.06.2021): 407. http://dx.doi.org/10.3390/ijgi10060407.
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The presented study aims to investigate the relationship between the use of emojis in location-based social media and the location of the corresponding post in terms of perceived objects and conducted activities connected to this place. The basis for this is not a purely frequency-based assessment, but a specifically introduced measure called typicality. To evaluate the typicality measure and examine the assumption that emojis are contextual indicants, a dataset of worldwide geotagged posts from Instagram relating to sunset and sunrise events is used, converted to a privacy-aware version based on a Hyperloglog approach. Results suggest that emojis can often provide more nuanced information about user activities and the surrounding environment than is possible with hashtags. Thus, emojis may be suitable for identifying less obvious characteristics and the sense of a place. Emojis are already explored in research, but mainly for sentiment analysis, for semantic studies or as part of emoji prediction. In contrast, this work provides novel insights into the user’s spatial or activity context by applying the typicality measure and therefore considers emojis contextual indicants.
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Almalki, Jameel. "A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets". PeerJ Computer Science 8 (26.07.2022): e1047. http://dx.doi.org/10.7717/peerj-cs.1047.
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Social media platforms such as Twitter, YouTube, Instagram and Facebook are leading sources of large datasets nowadays. Twitter’s data is one of the most reliable due to its privacy policy. Tweets have been used for sentiment analysis and to identify meaningful information within the dataset. Our study focused on the distance learning domain in Saudi Arabia by analyzing Arabic tweets about distance learning. This work proposes a model for analyzing people’s feedback using a Twitter dataset in the distance learning domain. The proposed model is based on the Apache Spark product to manage the large dataset. The proposed model uses the Twitter API to get the tweets as raw data. These tweets were stored in the Apache Spark server. A regex-based technique for preprocessing removed retweets, links, hashtags, English words and numbers, usernames, and emojis from the dataset. After that, a Logistic-based Regression model was trained on the pre-processed data. This Logistic Regression model, from the field of machine learning, was used to predict the sentiment inside the tweets. Finally, a Flask application was built for sentiment analysis of the Arabic tweets. The proposed model gives better results when compared to various applied techniques. The proposed model is evaluated on test data to calculate Accuracy, F1 Score, Precision, and Recall, obtaining scores of 91%, 90%, 90%, and 89%, respectively.
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Madderi Sivalingam, Saravanan, Smitha Ponnaiyan Sarojam, Malathi Subramanian i Kalachelvi Thulasingam. "A new mining and decoding framework to predict expression of opinion on social media emoji’s using machine learning models". IAES International Journal of Artificial Intelligence (IJ-AI) 13, nr 4 (1.12.2024): 5005. http://dx.doi.org/10.11591/ijai.v13.i4.pp5005-5012.
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<span lang="EN-US">This research work proposes a new framework mining and decoding (MindE) to predict the expression of opinion on social media emojis using machine learning (ML) models. Expression of opinion can be predicted with short messages on social media. This study used two groups of ML algorithms, convolutional neural network (CNN) ImageNet and CNN AlexNet classifier, and finally, applied the decision tree classifier to predict the type of expression. A recent dataset was taken from Kaggle, an open-source dataset consisting of 7476 rows of emojis for expression of opinion prediction. Accuracy was computed with a G power of 80%, and the experiment was repeated 20 times using both models. After the introduction of the proposed MindE framework, the performance of an expression of opinion prediction will be analyzed with accuracy level. The CNN ImageNet achieved an impressive 97.32% accuracy, whereas the CNN AlexNet algorithm reached only 85.98%. The independent sample T Test indicated a p-value of 0.001, which is below the significance level of 0.05. This suggests that the performance difference between the two ML algorithms is statistically significant. Consequently, the results strongly support the proposed framework “MindE” to predict the expression of opinion on social media emojis.</span>
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Anu Kiruthika M. i Angelin Gladston. "Implementation of Recurrent Network for Emotion Recognition of Twitter Data". International Journal of Social Media and Online Communities 12, nr 1 (styczeń 2020): 1–13. http://dx.doi.org/10.4018/ijsmoc.2020010101.
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A new generation of emoticons, called emojis, is being largely used for both mobile and social media communications. Emojis are considered a graphic expression of emotions, and users have been widely used to express their emotions in social media. Emojis are graphic unicode symbols used to express perceptions, views, and ideas as a shorthand. Unlike the small number of well-known emoticons carrying clear emotional content, hundreds of emojis are being used in different social networks. The task of emoji emotion recognition is to predict the original emoji in a tweet. Recurrent neural network is used for building emoji emotion recognition system. Glove is a word-embedding method used for obtaining vector representation of words and are used for training the recurrent neural network. This is achieved by mapping words into a meaningful space where the distance between words is related to semantic similarity. Based on the word embedding in the Twitter dataset, recurrent neural network builds the model and finally predicts the emoji associated with the tweets with an accuracy of 83%.
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Chen, Zhenpeng, Yanbin Cao, Huihan Yao, Xuan Lu, Xin Peng, Hong Mei i Xuanzhe Liu. "Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication Data". ACM Transactions on Software Engineering and Methodology 30, nr 2 (marzec 2021): 1–48. http://dx.doi.org/10.1145/3424308.
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Sentiment and emotion detection from textual communication records of developers have various application scenarios in software engineering (SE). However, commonly used off-the-shelf sentiment/emotion detection tools cannot obtain reliable results in SE tasks and misunderstanding of technical knowledge is demonstrated to be the main reason. Then researchers start to create labeled SE-related datasets manually and customize SE-specific methods. However, the scarce labeled data can cover only very limited lexicon and expressions. In this article, we employ emojis as an instrument to address this problem. Different from manual labels that are provided by annotators, emojis are self-reported labels provided by the authors themselves to intentionally convey affective states and thus are suitable indications of sentiment and emotion in texts. Since emojis have been widely adopted in online communication, a large amount of emoji-labeled texts can be easily accessed to help tackle the scarcity of the manually labeled data. Specifically, we leverage Tweets and GitHub posts containing emojis to learn representations of SE-related texts through emoji prediction. By predicting emojis containing in each text, texts that tend to surround the same emoji are represented with similar vectors, which transfers the sentiment knowledge contained in emoji usage to the representations of texts. Then we leverage the sentiment-aware representations as well as manually labeled data to learn the final sentiment/emotion classifier via transfer learning. Compared to existing approaches, our approach can achieve significant improvement on representative benchmark datasets, with an average increase of 0.036 and 0.049 in macro-F1 in sentiment and emotion detection, respectively. Further investigations reveal that the large-scale Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource but try to transform knowledge from the open domain through ubiquitous signals such as emojis. Finally, we present the open challenges of sentiment and emotion detection in SE through a qualitative analysis of texts misclassified by our approach.
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Tang, Hongmei, Wenzhong Tang, Dixiongxiao Zhu, Shuai Wang, Yanyang Wang i Lihong Wang. "EMFSA: Emoji-based multifeature fusion sentiment analysis". PLOS ONE 19, nr 9 (19.09.2024): e0310715. http://dx.doi.org/10.1371/journal.pone.0310715.
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Short texts on social platforms often suffer from insufficient emotional semantic expressions, sparse features, and polysemy. To enhance the accuracy achieved by sentiment analysis for short texts, this paper proposes an emoji-based multifeature fusion sentiment analysis model (EMFSA). The model mines the sentiments of emojis, topics, and text features. Initially, a pretraining method for feature extraction is employed to enhance the semantic expressions of emotions in text by extracting contextual semantic information from emojis. Following this, a sentiment- and emoji-masked language model is designed to prioritize the masking of emojis and words with implicit sentiments, focusing on learning the emotional semantics contained in text. Additionally, we proposed a multifeature fusion method based on a cross-attention mechanism by determining the importance of each word in a text from a topic perspective. Next, this method is integrated with the original semantic information of emojis and the enhanced text features, attaining improved sentiment representation accuracy for short texts. Comparative experiments conducted with the state-of-the-art baseline methods on three public datasets demonstrate that the proposed model achieves accuracy improvements of 2.3%, 10.9%, and 2.7%, respectively, validating its effectiveness.
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Husain, Fatemah, i Ozlem Uzuner. "Investigating the Effect of Preprocessing Arabic Text on Offensive Language and Hate Speech Detection". ACM Transactions on Asian and Low-Resource Language Information Processing 21, nr 4 (31.07.2022): 1–20. http://dx.doi.org/10.1145/3501398.
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Preprocessing of input text can play a key role in text classification by reducing dimensionality and removing unnecessary content. This study aims to investigate the impact of preprocessing on Arabic offensive language classification. We explore six preprocessing techniques: conversion of emojis to Arabic textual labels, normalization of different forms of Arabic letters, normalization of selected nouns from dialectal Arabic to Modern Standard Arabic, conversion of selected hyponyms to hypernyms, hashtag segmentation, and basic cleaning such as removing numbers, kashidas, diacritics, and HTML tags. We also experiment with raw text and a combination of all six preprocessing techniques. We apply different types of classifiers in our experiments including traditional machine learning, ensemble machine learning, Artificial Neural Networks, and Bidirectional Encoder Representations from Transformers (BERT)-based models to analyze the impact of preprocessing. Our results demonstrate significant variations in the effects of preprocessing on each classifier type and on each dataset. Classifiers that are based on BERT do not benefit from preprocessing, while traditional machine learning classifiers do. However, these results can benefit from validation on larger datasets that cover broader domains and dialects.
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Yang, Senqi, Xuliang Duan, Zeyan Xiao, Zhiyao Li, Yuhai Liu, Zhihao Jie, Dezhao Tang i Hui Du. "Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN". International Journal of Environmental Research and Public Health 19, nr 20 (19.10.2022): 13520. http://dx.doi.org/10.3390/ijerph192013520.
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Nowadays, tourists increasingly prefer to check the reviews of attractions before traveling to decide whether to visit them or not. To respond to the change in the way tourists choose attractions, it is important to classify the reviews of attractions with high precision. In addition, more and more tourists like to use emojis to express their satisfaction or dissatisfaction with the attractions. In this paper, we built a dataset for Chinese attraction evaluation incorporating emojis (CAEIE) and proposed an explicitly n-gram masking method to enhance the integration of coarse-grained information into a pre-training (ERNIE-Gram) and Text Graph Convolutional Network (textGCN) (E2G) model to classify the dataset with a high accuracy. The E2G preprocesses the text and feeds it to ERNIE-Gram and TextGCN. ERNIE-Gram was trained using its unique mask mechanism to obtain the final probabilities. TextGCN used the dataset to construct heterogeneous graphs with comment text and words, which were trained to obtain a representation of the document output category probabilities. The two probabilities were calculated to obtain the final results. To demonstrate the validity of the E2G model, this paper was compared with advanced models. After experiments, it was shown that E2G had a good classification effect on the CAEIE dataset, and the accuracy of classification was up to 97.37%. Furthermore, the accuracy of E2G was 1.37% and 1.35% ahead of ERNIE-Gram and TextGCN, respectively. In addition, two sets of comparison experiments were conducted to verify the performance of TextGCN and TextGAT on the CAEIE dataset. The final results showed that ERNIE and ERNIE-Gram combined TextGCN and TextGAT, respectively, and TextGCN performed 1.6% and 2.15% ahead. This paper compared the effects of eight activation functions on the second layer of the TextGCN and the activation-function-rectified linear unit 6 (RELU6) with the best results based on experiments.
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Al-Mutawa, Rihab Fahd, i Arwa Yousef Al-Aama. "User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques". Applied Sciences 13, nr 10 (12.05.2023): 5985. http://dx.doi.org/10.3390/app13105985.
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Opinion mining refers to the process that helps to identify and to classify users’ emotions and opinions from any source, such as an online review. Thus, opinion mining provides organizations with an insight into their reputation based on previous customers’ opinions regarding their services or products. Automating opinion mining in different languages is still an important topic of interest for scientists, including those using the Arabic language, especially since potential customers mostly do not rate their opinion explicitly. This study proposes an ensemble-based deep learning approach using fastText embeddings and the proposed Arabic emoji and emoticon opinion lexicon to predict user opinion. For testing purposes, the study uses the publicly available Arabic HARD dataset, which includes hotel reviews associated with ratings, starting from one to five. Then, by employing multiple Arabic resources, it experiments with different generated features from the HARD dataset by combining shallow learning with the proposed approach. To the best of our knowledge, this study is the first to create a lexicon that considers emojis and emoticons for its user opinion prediction. Therefore, it is mainly a helpful contribution to the literature related to opinion mining and emojis and emoticons lexicons. Compared to other studies found in the literature related to the five-star rating prediction using the HARD dataset, the accuracy of the prediction using the proposed approach reached an increase of 3.21% using the balanced HARD dataset and an increase of 2.17% using the unbalanced HARD dataset. The proposed work can support a new direction for automating the unrated Arabic opinions in social media, based on five rating levels, to provide potential stakeholders with a precise idea about a service or product quality, instead of spending much time reading other opinions to learn that information.
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Kulkongkoon, Theerawee, Nagul Cooharojananone i Rajalida Lipikorn. "Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images". International Journal of Electrical and Computer Engineering (IJECE) 14, nr 1 (1.02.2024): 698. http://dx.doi.org/10.11591/ijece.v14i1.pp698-710.
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Emojis are any small images, symbols, or icons that are used in social media. Several well-known emojis have been ranked and sentiment scores have been assigned to them. These ranked emojis can be used for sentiment analysis; however, many new released emojis have not been ranked and have no sentiment score yet. This paper proposes a new method to estimate the sentiment score of any unranked emotion emoji from its image by classifying it into the class of the most similar ranked emoji and then estimating the sentiment score using the score of the most similar emoji. The accuracy of sentiment score estimation is improved by using multi-scale images. The ranked emoji image data set consisted of 613 classes with 161 emoji images from three different platforms in each class. The images were cropped to produce multi-scale images. The classification and estimation were performed by using convolutional neural network (CNN) with multi-scale emoji images and the proposed voting algorithm called the majority voting with probability (MVP). The proposed method was evaluated on two datasets: ranked emoji images and unranked emoji images. The accuracies of sentiment score estimation for the ranked and unranked emoji test images are 98% and 51%, respectively.
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Balcıoğlu, Yavuz Selim, Yelda Özkoçak, Yağmur Gümüşboğa i Erkut Altındağ. "From Symbols to Emojis: Analyzing Visual Communication Trends on Social Media". Studies in Media and Communication 13, nr 2 (19.03.2025): 250. https://doi.org/10.11114/smc.v13i2.7509.
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This study explores the transformative role of emojis in digital communication, focusing on their use on Facebook and Instagram. Using a dual theoretical lens of social constructivist theory and visual semiotics, the research examines how emojis function as socially constructed symbols that convey emotions, enhance textual communication, and foster cultural connections. Social constructivist theory emphasizes the collaborative construction of meaning, while visual semiotics focuses on the cultural and contextual interpretations of visual signs, including emoticons.The analysis includes a dataset of 66,053 comments, with 29,628 from Facebook and 36,425 from Instagram, collected using purposive sampling methods. Text mining and natural language processing (NLP) techniques were used to identify and interpret emoji usage patterns, revealing Instagram as the dominant platform for emoji-based communication, accounting for 55.17% of the data. The study shows that emojis serve as essential tools for expressing emotions, building connections, and transcending language barriers, contributing to the emergence of a universal visual language.The findings suggest that emoji are not only replacing textual elements, but also adding layers of emotional depth, clarity, and cultural nuance to digital interactions. Popular emojis such as the "face with tears of joy" and the "heart" are universally used, but exhibit contextual variations across platforms. This research underscores the growing importance of visual communication in the digital age and calls for further exploration of the cultural, linguistic, and communicative implications of emoji use on global digital discourse.
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Koltsova, Elena A., i Faina I. Kartashkova. "Digital Communication and Multimodal Features: Functioning of Emoji in Interpersonal Communication". RUDN Journal of Language Studies, Semiotics and Semantics 13, nr 3 (30.09.2022): 769–83. http://dx.doi.org/10.22363/2313-2299-2022-13-3-769-783.
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Technical advances and digital means of communication have led to the development of digital semiotics which is characterised by its multimodality and abounds in paralinguistic elements such as emojis, emoticons, memes, etc. These extralinguistic elements serve as a compensatory mechanism in the new communication means. The increasing interest of users in various iconic signs and symbols generates the research interest in different fields of knowledge. The study aims to consider cognitive, semiotic and psycholinguistic features of emojis in interpersonal communication through analysing their functions in text messages and in social network messages. An attempt to reveal their persuasive mechanism is made. The research is based on a large scale dataset comprised of the private text messages as well as public posts on social networks which include verbal and nonverbal / iconic elements. The research data presents a multilingual bank of English, Russian and French sources. The research methods include context analysis, linguistic and pragmatic analysis and content analysis. The findings show that emojis in private interpersonal communication perform a number of functions, namely nonverbal, emotive, pragmatic, punctuation, substitutional, decorative and rhetorical functions. These iconic symbols incorporated in the interpersonal digital communication present a compensatory mechanism and the means of persuasion of a message addressee / recipient. The combination of verbal and iconic elements triggers a double focusing mechanism, and the perception is shaped by all cognitive mechanisms including rational and emotional, unconscious components.
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Alturayeif, Nora, i Hamzah Luqman. "Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function". Applied Sciences 11, nr 22 (12.11.2021): 10694. http://dx.doi.org/10.3390/app112210694.
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The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.
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Vimala, Dhulepalla. "Detection of Fake Online Reviews Using Semi Supervised and Supervised Learning". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 04 (25.04.2024): 1–5. http://dx.doi.org/10.55041/ijsrem31613.
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Consumers reviews on ecommerce websites, online services, ratings and experience stories are useful for the user as well as the vendor. The reviewer can increase their brand’s loyalty and help other customers understand their experience with the product. Similarly reviews help the vendors gain more profiles by increasing their sale of products, if consumers leave positive feedback on their product review. But unfortunately, these review mechanisms can be misused by vendors. For example, one may create fake positive reviews to promote brand’s reputation or try to demote competitor’s products by leaving fake negative reviews on their product. Existing solutions with supervised include application of different machine learning algorithms and different tools like Weka. Unlike the existing work, instead of using a constrained dataset I chose to have a wide variety of vocabulary to work on such as different subjects of datasets combined as one big data set. Sentiment analysis has been incorporated based on emojis and text content in the reviews. Fake reviews are detected and categorized. The testing results are obtained through the application of Naïve Bayes, Linear SVC, Support Vector Machine and Random forest algorithms. The implemented (proposed) solution is to classify these reviews into fake or genuine. The highest accuracy is obtained by using Naïve Bayes by including sentiment classifier.
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Alawneh, Hussam, Ahmad Hasasneh i Mohammed Maree. "On the Utilization of Emoji Encoding and Data Preprocessing with a Combined CNN-LSTM Framework for Arabic Sentiment Analysis". Modelling 5, nr 4 (7.10.2024): 1469–89. http://dx.doi.org/10.3390/modelling5040076.
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Social media users often express their emotions through text in posts and tweets, and these can be used for sentiment analysis, identifying text as positive or negative. Sentiment analysis is critical for different fields such as politics, tourism, e-commerce, education, and health. However, sentiment analysis approaches that perform well on English text encounter challenges with Arabic text due to its morphological complexity. Effective data preprocessing and machine learning techniques are essential to overcome these challenges and provide insightful sentiment predictions for Arabic text. This paper evaluates a combined CNN-LSTM framework with emoji encoding for Arabic Sentiment Analysis, using the Arabic Sentiment Twitter Corpus (ASTC) dataset. Three experiments were conducted with eight-parameter fusion approaches to evaluate the effect of data preprocessing, namely the effect of emoji encoding on their real and emotional meaning. Emoji meanings were collected from four websites specialized in finding the meaning of emojis in social media. Furthermore, the Keras tuner optimized the CNN-LSTM parameters during the 5-fold cross-validation process. The highest accuracy rate (91.85%) was achieved by keeping non-Arabic words and removing punctuation, using the Snowball stemmer after encoding emojis into Arabic text, and applying Keras embedding. This approach is competitive with other state-of-the-art approaches, showing that emoji encoding enriches text by accurately reflecting emotions, and enabling investigation of the effect of data preprocessing, allowing the hybrid model to achieve comparable results to the study using the same ASTC dataset, thereby improving sentiment analysis accuracy.
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N, Bagiyalakshmi, i A. C. Kothandaraman. "Optimized Deep Learning Model for Sentimental Analysis to Improve Consumer Experience in E-Commerce Websites". Journal of Computer Allied Intelligence 2, nr 3 (30.06.2024): 41–54. http://dx.doi.org/10.69996/jcai.2024014.
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Sentiment analysis plays a pivotal role in deciphering customer sentiments from vast amounts of unstructured data, particularly in the context of e-commerce where customer reviews are prolific. The evolution of e-commerce reviews toward a multimodal format, including images, videos, and emojis, introduces new dimensions to sentiment analysis. Traditional text-based models may struggle to effectively capture sentiments expressed through non-textual elements. This paper proposed an effective sentiment analysis model for the E-Commerce Platform to improve the user consumer experience. The proposed method comprises Fejer Kernel filtering for data points estimation in the E-commerce dataset points. Within the estimated data points fuzzy dictionary-based semantic word feature extraction is performed for the estimation of features in the E-Commerce dataset. The dataset for the analysis is computed with the Optimized Stimulated Annealing for the feature extraction and selection. The classification of customer opinion is classified with the BERT deep learning model. The feature extracted from the model is the opinion of consumers in the E-Commerce dataset. The classification of consumer preference experience is based on opinion of customers in the E-commerce dataset. Simulation results demonstrated that proposed model achieves the higher classification accuracy for the E-Commerce platform.
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Kong, Jeffery T. H., Filbert H. Juwono, Ik Ying Ngu, I. Gde Dharma Nugraha, Yan Maraden i W. K. Wong. "A Mixed Malay–English Language COVID-19 Twitter Dataset: A Sentiment Analysis". Big Data and Cognitive Computing 7, nr 2 (27.03.2023): 61. http://dx.doi.org/10.3390/bdcc7020061.
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Social media has evolved into a platform for the dissemination of information, including fake news. There is a lot of false information about the current situation of the Coronavirus Disease 2019 (COVID-19) pandemic, such as false information regarding vaccination. In this paper, we focus on sentiment analysis for Malaysian COVID-19-related news on social media such as Twitter. Tweets in Malaysia are often a combination of Malay, English, and Chinese with plenty of short forms, symbols, emojis, and emoticons within the maximum length of a tweet. The contributions of this paper are twofold. Firstly, we built a multilingual COVID-19 Twitter dataset, comprising tweets written from 1 September 2021 to 12 December 2021. In particular, we collected 108,246 tweets, with over 67% in Malay language, 27% in English, 2% in Chinese, and 4% in other languages. We then manually annotated and assigned the sentiment of 11,568 tweets into three-class sentiments (positive, negative, and neutral) to develop a Malay-language sentiment analysis tool. For this purpose, we applied a data compression method using Byte-Pair Encoding (BPE) on the texts and used two deep learning approaches, i.e., the Multilingual Bidirectional Encoder Representation for Transformer (M-BERT) and convolutional neural network (CNN). BPE tokenization is used to encode rare and unknown words into smaller meaningful subwords. With the CNN, we converted the labeled tweets into image files. Our experiments explored different BPE vocabulary sizes with our BPE-Text-to-Image-CNN and BPE-M-BERT models. The results show that the optimal vocabulary size for BPE is 12,000; any values beyond that would not contribute much to the F1-score. Overall, our results show that BPE-M-BERT slightly outperforms the CNN model, thereby showing that the pre-trained M-BERT network has the advantage for our multilingual dataset.
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Ji, Houjun. "Depression Detection on Twitter Text Based on Negative Emotion Score and Level of Depressed". Highlights in Science, Engineering and Technology 81 (26.01.2024): 368–73. http://dx.doi.org/10.54097/ensgdz34.
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This essay presents a method for detecting depression level detection based on the negative emotion score of social media text. The score is calculated by the NRC Word-Emotion Association Lexicon (EmoLex). The dataset will first be processed by the EmoLex model and then based on certain scores given by the model, three levels of depression will be generated for further classification. The advantage of this model is that the detected depression is classified into three levels, which can help people take different levels of response. The experiment is performed in Multi Labeled Depression Corpus of 60,000 English tweets. Three models are trained in this experiment: Random Forest and Logistic Regression. The result shows that Random Forest achieves 65.9% accuracy; Logistic Regression achieve 72.2% accuracy.
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Arcenal, Erika Kristine E., Licca Pauleen V. Capistrano, Marielle Jessie D. De Guzman, Micaela Isabel M. Forrosuelo i Janeson M. Miranda. "Comparative Analysis of Reddit Posts and ChatGPT-Generated Texts’ Linguistic Features: A Short Report on Artificial Intelligence’s Imitative Capabilities". International Journal of Multidisciplinary: Applied Business and Education Research 5, nr 9 (23.09.2024): 3475–81. http://dx.doi.org/10.11594/ijmaber.05.09.06.
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In recent years, the unprecedented explosion of artificial intelligence (AI), particularly generative AI, has dramatically and drastically altered many human fields, posing queries about how generative AI can imitate human language. Given the newness of generative AI as a controversial phenomenon, there is an urgency to closely examine how its linguistic outputs could mimic human language produced in natural contexts. Hence, in this short report, we discuss the observed similarities and differences in the linguistic features of the subreddit r/Marriage spouse appreciatory posts and ChatGPT-4 outputs. These results were the offshoot of our genre analysis on these two linguistic data sets. Our analysis revealed that ChatGPT-4 generated texts contain impeccable grammar, while the Reddit appreciatory posts have grammatical discrepancies, such as errors in subject-verb agreement, improper punctuation marks, and erroneous capitalization; ChatGPT-4 generated texts have more complex syntactical structure; Reddit dataset utilized more internet jargon, slang, and profanities and seems to be unpredictable and arbitrary in terms of textual length; and ChatGPT-4 outputs appear to overuse emojis while underuse emoticons and tend to use these digital linguistic elements without regard to their proper contexts. In light of these results, we claim that AI-generated texts, although they can mimic human language, this is on a mere surface level, and a closer inspection could uncover distinct variations. We recommend that future studies use more comprehensive and different datasets and continuously employ comparative and contrastive linguistic analysis to further investigate AI’s imitative capabilities.
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Nikhil,, Navneet. "Hate Speech Detection". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 05 (27.05.2024): 1–5. http://dx.doi.org/10.55041/ijsrem34783.
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The massive expansion of social networking websites has made it easier for people with various cultural and psychological backgrounds to communicate directly with one another. It has led to an increase in online conflicts between them. This paper proposes an approach to detect hate speech. A publicly available dataset of tweets in language is used. Data preprocessing includes the removal of stopwords , punctuations , emojis , numbers , URLs etc. and feature extraction is carried out using tokenization , lemmatization and POS tagging. The performances of XGBoost, Random Forest , Logistic Regression and SVM have been compared in this study for the detection of hate speech. XGBoost classifier provided highest accuracy of 74.93%. Keywords: Hate speech, Hate tweets, Machine learning.
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Vayadande, Kuldeep, Aditya Bodhankar, Ajinkya Mahajan, Diksha Prasad, Shivani Mahajan, Aishwarya Pujari i Riya Dhakalkar. "Classification of Depression on social media using Distant Supervision". ITM Web of Conferences 50 (2022): 01005. http://dx.doi.org/10.1051/itmconf/20225001005.
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Amidst Covid-19, young adults have experienced major symptoms of anxiety and/or depression disorder (56%). Mental health issues have been spiking all over the world rapidly. People have taken up to social media as a platform to vent about their mental breakdowns. Twitter has seen enormous rise in depressive and anxious tweets in these times, but the downside being that majority of the population has neglected the importance of mental health issues and there are not enough resources to liberate people about it. Also, people hesitate to talk about their mental issues and seek help. So, a machine learning model using distant supervision to detect depression on Twitter is curated. Use of Sentiment140 dataset with 1.6 million records of different tweets. Our training data makes use of Twitter tweets included with emojis, which are classified as noisy labels on a dataset. Further, this paper mentions about how to use models like Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Random Forest, XGBoost to distinguishing tweets between depressive or nondepressive. The purpose behind using multiple models is to achieve highest accuracy when trained with emoticon dataset. The paper’s main contribution is the idea of using tweets with emoticons for distant supervised learning.
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Kim, Ye-Hyun, Jungwon Cho i Jaechoon Jo. "Development of Real-time Soccer Match Comment Sentiment Analysis and Emoji Conversion System". International Journal on Advanced Science, Engineering and Information Technology 14, nr 6 (25.12.2024): 2114–20. https://doi.org/10.18517/ijaseit.14.6.11792.
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This study proposes an innovative system designed to automatically analyze emotions in real-time comments during live sports broadcasts, particularly soccer matches, and convert them into appropriate emojis. This approach aims to overcome the limitations of viewer interaction and enable seamless emotional communication across language barriers. The system utilizes the KoBERT model, optimized for Korean natural language processing, for accurate text-based sentiment analysis. The real-time emoji conversion and display functionality is implemented using the React framework, and web socket technology is employed to achieve low-latency data processing in real-time. The model was trained using a large Korean conversation dataset and achieved an emotion classification accuracy of 71.12%. In terms of performance, the system can process 308.71 comments per second with an average latency of 37.52 milliseconds, proving its effectiveness in a live sports broadcast environment. The proposed system enhances the viewing experience by allowing users to express emotions intuitively, thus breaking the limitations of text-based communication. This system introduces a new paradigm for audience interaction in live sports broadcasts, promoting a more inclusive and engaging experience. Using emojis, which transcend language barriers, viewers can share their emotions without text, fostering real-time emotional exchange among a global audience. The research highlights the practical application of real-time text and sentiment analysis technologies. It provides a foundation for future enhancements, such as support for multiple languages and more advanced real-time data processing capabilities.
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Pravin D. Kaware. "Indo-HateSpeech Analysis: A Multi-Level Hate Speech Classification Framework Using BERT Features and Machine Learning Models". Advances in Nonlinear Variational Inequalities 28, nr 5s (24.01.2025): 405–18. https://doi.org/10.52783/anvi.v28.3912.
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The rise of social media has enabled individuals to express opinions widely, but it has also facilitated the spread of hate speech, posing a significant societal challenge. Effective automated hate speech detection systems are crucial to mitigating this issue. Addressing this issue in the context of diverse languages like those in India, this paper presented Indo-HateSpeech, a multi-level framework that combines contextualized embeddings from Bidirectional Encoder Representations from Transformers (BERT) with classical machine learning models for hate speech classification. The comprehensive dataset including emojis, emoticons, hashtags, and slang is necessary to identify hate speech on social media based on current trends. Therefore, the Indo-HateSpeech dataset is developed and collected from various posts on Instagram. This dataset contains hate speech sentences in English and Hindi in Indian Context for classifying hate speech into three categories: No Hate (HS0), Hate (HS1), and Extreme Hate (HSN). Contextual embeddings derived from the pre-trained BERT model to extract high-quality text features from text data with sentiment features. These embeddings are subsequently fed into traditional machine learning classifiers, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM), for classification. Experimental results demonstrate the effectiveness of BERT embeddings in enhancing classification performance in terms of accuracy, F1-score, and recall over traditional approaches, highlighting the framework's potential for scalable deployment in real-world applications.
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Sudeep K. Hase. "Sentiment Classification on Multivariate Feature Selection on Social Media dataset using Hybrid Machine Learning Techniques". Journal of Information Systems Engineering and Management 10, nr 1s (30.12.2024): 525–39. https://doi.org/10.52783/jisem.v10i1s.234.
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Sentiment classification is a crucial component of natural language processing that focuses on analyzing and classifying the emotional tone conveyed in text data. With the rapid proliferation of social media platforms, the ability to accurately discern public sentiment has become vital for applications spanning marketing, political forecasting, and public opinion analysis. This abstract delves into the implementation of hybrid machine learning techniques for sentiment classification, leveraging multivariate feature selection methods on diverse social media datasets. Traditional machine learning models, though effective, often struggle with the complexity and high dimensionality of social media data, which may include text, emojis, images, and metadata. A hybrid machine learning approach, combining the strengths of various models, addresses these challenges by optimizing both feature selection and classification accuracy. The proposed framework begins with robust data preprocessing, including text normalization and tokenization. Advanced feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF), word embeddings (Word2Vec, GloVe), and sentiment lexicons are utilized to capture the intricate semantic characteristics of the text. For multivariate feature selection, techniques such as Recursive Feature Elimination (RFE), Chi-square tests, and correlation-based feature selection (CFS) are employed to identify and retain the most informative features, thereby improving model efficiency. The classification stage integrates hybrid models, combining the predictive power of algorithms such as Support Vector Machines (SVM), Random Forests, and ensemble learning methods (e.g., gradient boosting). These models are tuned using cross-validation and grid search to enhance generalization performance. The hybrid approach demonstrates superior performance in terms of accuracy, precision, recall, and F1-score compared to standalone machine learning models. The combination of comprehensive feature selection and robust classification algorithms effectively mitigates overfitting and enhances scalability. Empirical results from experiments on real-world social media datasets indicate that the proposed method is adept at capturing nuanced sentiment variations and ensuring high classification accuracy, proving its effectiveness for dynamic and large-scale data analysis.
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Cerrahoğlu, Enes, i Pınar Cihan. "Sentiment Analysis and Emojification of Tweets". International Conference on Pioneer and Innovative Studies 1 (13.06.2023): 481–86. http://dx.doi.org/10.59287/icpis.876.
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– Social media platforms have become a prevalent means for individuals to share their emotionsand thoughts. With millions of tweets being posted on Twitter every day, these tweets provide us with avast dataset. Conducting sentiment analysis on this dataset can be a valuable method to obtain meaningfulinsights about societal trends. For this purpose, a sentiment analysis model and a web interface thatemojifies emotions were developed using the Python programming language. This model works on tweetsshared on Twitter and utilizes natural language processing techniques to determine the sentiment of thetweets. In this study, 168.274 English tweets were collected using the Twitter API. The collected tweetsunderwent a cleaning process where URLs, hashtags, mentions, and emojis were removed. Then, theTextBlob Python library was employed to label the tweets as positive, negative, or neutral. The labeledtweets were subjected to classification accuracy testing using Gradient Boosting, Logistic Regression,Naive Bayes, Random Forest, and Support Vector Machines machine learning models. The findingsrevealed that logistic regression achieved the highest classification accuracy with 94%. Lastly, a webinterface was developed, which retrieves the last 50 tweets of a queried user's profile and appends a relevantemoji based on the sentiment of each tweet.
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Fadhli, Imen, Lobna Hlaoua i Mohamed Nazih Omri. "Sentiment Analysis CSAM Model to Discover Pertinent Conversations in Twitter Microblogs". International Journal of Computer Network and Information Security 14, nr 5 (8.10.2022): 28–46. http://dx.doi.org/10.5815/ijcnis.2022.05.03.
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In recent years, the most exploited sources of information such as Facebook, Instagram, LinkedIn and Twitter have been considered to be the main sources of misinformation. The presence of false information in these social networks has a very negative impact on the opinions and the way of thinking of Internet users. To solve this problem of misinformation, several techniques have been used and the most popular is the sentiment analysis. This technique, which consists in exploring opinions on corpora of texts, has become an essential topic in this field. In this article, we propose a new approach, called Conversational Sentiment Analysis Model (CSAM), allowing, from a text written on a subject through messages exchanged between different users, called a conversation, to find the passages describing feelings, emotions, opinions and attitudes. This approach is based on: (i) the conditional probability in order to analyse sentiments of different conversation items in Twitter microblog, which are characterized by small sizes, the presence of emoticons and emojis, (ii) the aggregation of conversation items using the uncertainty theory to evaluate the general sentiment of conversation. We conducted a series of experiments based on the standard Semeval2019 datasets, using three standard and different packages, namely a library for sentiment analysis TextBlob, a dictionary, a sentiment reasoner Flair and an integration-based framework for the Vader NLP task. We evaluated our model with two dataset SemEval 2019 and ScenarioSA, the analysis of the results, which we obtained at the end of this experimental study, confirms the feasibility of our model as well as its performance in terms of precision, recall and F-measurement.
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Zbiri, Asmae, Azeddine Hachmi, Dominique Haesen i Fatima Ezzahrae El Alaoui-Faris. "New Investigation and Challenge for Spatiotemporal Drought Monitoring Using Bottom-Up Precipitation Dataset (SM2RAIN-ASCAT) and NDVI in Moroccan Arid and Semi-Arid Rangelands". Ekológia (Bratislava) 41, nr 1 (1.03.2022): 90–100. http://dx.doi.org/10.2478/eko-2022-0010.
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Abstract Remotely sensed soil moisture products showed sensitivity to vegetation cover density and soil typology at regional dryland level. In these regions, drought monitoring is significantly performed using soil moisture index and rainfall data. Recently, rainfall and soil moisture observations have increasingly become available. This has hampered scientific progress as regards characterization of land surface processes not just in meteorology. The purpose of this study was to investigate the relationship between a newly developed precipitation dataset, SM2RAIN (Advanced SCATterometer (SM2RAIN-ASCAT), and NDVI (eMODIS-TERRA) in monitoring drought events over diverse rangeland regions of Morocco. Results indicated that the highest polynomial correlation coefficient and the lowest root mean square error (RMSE) between SM2RAIN-ASCAT and NDVI were found in a 10-year period from 2007 to 2017 in all rangelands (R = 0.81; RMSE = 0.05). This relationship was strong for degraded rangeland, where there were strong positive correlation coefficients for NDVI and SM2RAIN (R = 0.99). High correlations were found for sparse and moderate correlations for shrub rangeland (R = 0.82 and 0.61, respectively). The anomalies maps showed a very good similarity between SM2RAIN and Normalized Difference Vegetation Index (NDVI) data. The results revealed that the SM2RAIN-ASCAT and NDVI product could accurately predict drought events in arid and semi-arid rangelands.
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Mayor, Eric, i Lucas M. Bietti. "Twitter, time and emotions". Royal Society Open Science 8, nr 5 (maj 2021): 201900. http://dx.doi.org/10.1098/rsos.201900.
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The study of temporal trajectories of emotions shared in tweets has shown that both positive and negative emotions follow nonlinear circadian (24 h) and circaseptan (7-day) patterns. But to this point, such findings could be instrument-dependent as they rely exclusively on coding using the Linguistic Inquiry Word Count. Further, research has shown that self-referential content has higher relevance and meaning for individuals, compared with other types of content. Investigating the specificity of self-referential material in temporal patterns of emotional expression in tweets is of interest, but current research is based upon generic textual productions. The temporal variations of emotions shared in tweets through emojis have not been compared to textual analyses to date. This study hence focuses on several comparisons: (i) between Self-referencing tweets versus Other topic tweets, (ii) between coding of textual productions versus coding of emojis, and finally (iii) between coding of textual productions using different sentiment analysis tools (the Linguistic Inquiry and Word Count—LIWC; the Valence Aware Dictionary and sEntiment Reasoner—VADER and the Hu Liu sentiment lexicon—Hu Liu). In a collection of more than 7 million Self-referencing and close to 18 million Other topic content-coded tweets, we identified that (i) similarities and differences in terms of shape and amplitude can be observed in temporal trajectories of expressed emotions between Self-referring and Other topic tweets, (ii) that all tools feature significant circadian and circaseptan patterns in both datasets but not always, and there is often a correspondence in the shape of circadian and circaseptan patterns, and finally (iii) that circadian and circaseptan patterns obtained from the coding of emotional expression in emojis sometimes depart from those of the textual analysis, indicating some complementarity in the use of both modes of expression. We discuss the implications of our findings from the perspective of the literature on emotions and well-being.
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Dolot, Dyea, i Arlene Opina. "Forms and Functions of Graphicons in Facebook Private Conversations Among Young Filipino Users". International Journal of Linguistics, Literature and Translation 4, nr 6 (30.06.2021): 62–73. http://dx.doi.org/10.32996/ijllt.2021.4.6.8.
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Understanding the functions of graphicons such as emojis, images, memes, videos, GIFs, emoticons, and stickers has become increasingly relevant as they have become extensively integrated into textual messages on Facebook, especially in group chats. This study aimed to investigate the forms and functions of graphicons used by young Filipino users (ages 18-31) on Facebook group chats. The datasets were extracted from the corpora, ten Facebook group chats, each lasting for three months, and analyzed using or computer-mediated discourse analysis or language-focused content analysis. According to the findings of this study, emoji was the most widely used graphicon by young Filipino users on Facebook, while sticker was the least. Adopting Herring and Dainas’ six functions of graphicons (2017), the researcher discovered additional five functions on Facebook group chats. These functions are identified as mention, reaction, riff, tone modification, action, narrative sequence, response, sharing, replacement, complement, and attention. It was also discovered that a graphicon could serve more than one function in a conversation. Tone modification was the most commonly used function, while the narrative sequence was the least. It was found out that in both emojis and emoticons, ‘tone modification’ was the most used function while ‘sharing’ in both images and videos. Meanwhile, ‘action’ was the most used function in GIFs, ‘attention’ in memes, and ‘mention’ in stickers. Because of the significantly increased use of online communication, this study may provide insight on how people may use these graphicons in their everyday conversations.
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Hachmi, Azeddine, Asmae Zbiri, Dominique Haesen, Fatima Ezzahrae El Alaoui-Faris i David A. Vaccari. "Performance Tests to Modeling Future Climate–vegetation Interactions in Virtual World: an Option for Application of Remote Sensed and Statistical Systems". WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 18 (31.12.2021): 178–89. http://dx.doi.org/10.37394/23209.2021.18.22.
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Working in the virtual world is different to real experiment in field. Nowadays, with remote sensing and new analysis programs we can assure a quick response and with less costs. The problem is efficiency of these methods and formulation of an exact response with low errors to manage an environmental risk. The objective of this article is to ask question about performance of some tools in this decision making in Morocco. The study uses (Test 1: TaylorFit Multivariate Polynomial Regressions (MPR); Test 2: SAS Neural Network (NN) to modeling relationship between European Center for Medium-Range Weather Forecasts dataset and NDVI eMODIS-TERRA at arid Eastern Morocco. The results revealed that the both test could accurately predict future scenario of water stress and livstock production decrease. The experience shows that virtual work with Artificial Intelligence is the future of ecological modeling and rapid decision-making in case of natural disasters.
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Alshaabi, Thayer, Jane L. Adams, Michael V. Arnold, Joshua R. Minot, David R. Dewhurst, Andrew J. Reagan, Christopher M. Danforth i Peter Sheridan Dodds. "Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter". Science Advances 7, nr 29 (lipiec 2021): eabe6534. http://dx.doi.org/10.1126/sciadv.abe6534.
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In real time, Twitter strongly imprints world events, popular culture, and the day-to-day, recording an ever-growing compendium of language change. Vitally, and absent from many standard corpora such as books and news archives, Twitter also encodes popularity and spreading through retweets. Here, we describe Storywrangler, an ongoing curation of over 100 billion tweets containing 1 trillion 1-grams from 2008 to 2021. For each day, we break tweets into 1-, 2-, and 3-grams across 100+ languages, generating frequencies for words, hashtags, handles, numerals, symbols, and emojis. We make the dataset available through an interactive time series viewer and as downloadable time series and daily distributions. Although Storywrangler leverages Twitter data, our method of tracking dynamic changes in n-grams can be extended to any temporally evolving corpus. Illustrating the instrument’s potential, we present example use cases including social amplification, the sociotechnical dynamics of famous individuals, box office success, and social unrest.
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Aribowo, Agus Sasmito, i Siti Khomsah. "Implementation Of Text Mining For Emotion Detection Using The Lexicon Method (Case Study: Tweets About Covid-19)". Telematika 18, nr 1 (16.03.2021): 49. http://dx.doi.org/10.31315/telematika.v18i1.4341.
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Information and news about Covid-19 received various responses from social media users, including Twitter users. Changes in netizen opinion from time to time are interesting to analyze, especially about the patterns of public sentiment and emotions contained in these opinions. Sentiment and emotional conditions can illustrate the public's response to the Covid-19 pandemic in Indonesia. This research has two objectives, first to reveal the types of public emotions that emerged during the Covid-19 pandemic in Indonesia. Second, reveal the topics or words that appear most frequently in each emotion class. There are seven types of emotions to be detected, namely anger, fear, disgust, sadness, surprise, joy, and trust. The dataset used is Indonesian-language tweets, which were downloaded from April to August 2020. The method used for the extraction of emotional features is the lexicon-based method using the EmoLex dictionary. The result obtained is a monthly graph of public emotional conditions related to the Covid-19 pandemic in the dataset.
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Hemanth, Bollepalli Sri Sai. "Whatsapp Chat Analyzer Using Machine Learning". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 04 (18.04.2024): 1–5. http://dx.doi.org/10.55041/ijsrem31024.
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WhatsApp has emerged as the go-to method for communication. Conversations on WhatsApp cover a wide range of topics among individuals or groups. This data can be valuable for advancing technologies like machine learning, which rely on quality data for effective learning experiences. Our tool is designed to offer comprehensive analysis of WhatsApp data, regardless of the subject of the conversation. By using our developed code, a deeper insight into the data can be achieved. One great benefit of this tool is that it utilizes common Python libraries like Pandas, Matplotlib, Seaborn, Streamlit, Numpy, Re, Emojis, and sentiment analysis to generate data frames and visualizations. These are then showcased in a streamlit web app that is efficient and requires fewer resources. This makes it ideal for analyzing large datasets. Key Words: Inspecting, Examining, Research, Data Analysis, Matplotlib, Pandas, Streamlit
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Fadlan Amrullah i Achmad Solichin. "Analisis Emosi Pada Live Chat Youtube 'Mata Najwa: 3 Bacapres Bicara Gagasan' Menggunakan Pendekatan Lexicon dan Algoritma Naive Bayes". Jurnal Ticom: Technology of Information and Communication 12, nr 3 (31.05.2024): 121–28. http://dx.doi.org/10.70309/ticom.v12i3.132.
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Pada tahun 2023, Indonesia memasuki periode politik krusial dalam ranah politik, menandai persiapan menuju pemilihan Presiden dan Wakil Presiden serta pemilu legislatif 2024. Dalam konteks ini, media sosial, khususnya YouTube, menjadi panggung utama interaksi politik. Mata Najwa, melalui kanal YouTube-nya, menjadi panggung sentral bagi interaksi politik dengan menyelenggarakan acara siaran langsung berjudul "3 Bacapres Bicara Gagasan" pada 19 September 2023. Pada kesempatan tersebut, para bakal calon Presiden berbagi gagasan dan pandangan langsung kepada masyarakat, memanfaatkan kemajuan teknologi komunikasi. Peran YouTube dalam lanskap politik semakin signifikan, dan respons emosional dalam live chat menjadi fokus analisis. Penelitian ini bertujuan untuk melakukan analisis emosi terhadap pandangan atau respon masyarakat kepada acara yang diselenggarakan pada kanal youtube Mata Najwa tersebut. Dengan memanfaatkan kamus kata EmoLex, analisis emosi pada dataset yang besar menjadi lebih efisien tanpa memerlukan pelabelan emosi secara manual. Pendekatan machine learning dilakukan melalui ekstraksi fitur TF-IDF dan penerapan Algoritma Multinomial Naive Bayes untuk menganalisis emosi dari teks komentar. Dataset yang digunakan bersumber dari live chat pada acara inti Mata Najwa, yaitu pada saat para bacapres bicara gagasan mereka (Anies Baswedan, Ganjar Pranowo, dan Prabowo Subianto). Dengan menerapkan ekstraksi fitur TF-IDF dan klasifikasi, model yang dikembangkan mencapai tingkat akurasi sebesar 90.67% berdasarkan dataset gabungan ke-tiga bakal calon Presiden
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He, Jiabei. "Analyzing film and drama reviews: Distinguishing trolls from genuine audience feedback based on the BERT model". Applied and Computational Engineering 53, nr 1 (28.03.2024): 213–19. http://dx.doi.org/10.54254/2755-2721/53/20241376.
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With the expanding influence of the Internet, an increasing number of individuals rely on viewer reviews to make informed decisions about whether to watch a movie or TV series. However, the prevalence of manipulated or "navy" reviews, employed by companies to boost their products' reputation, has created a significant challenge. While numerous studies have dissected film and drama reviews, a notable gap exists in discerning genuine audience feedback from deceptive ones. This article's research focus is on evaluating the model's capacity to effectively differentiate between authentic audience comments and navy reviews and delving into the complexities encountered when the model assesses comments, as well as highlighting the disparities between model-generated judgments and human assessments. This article first collects a large amount of different types of comment data, annotates these data, and then uses these data to train and fine tune the BERT model. Finally, the results are obtained and analyzed to determine the reasons. This article found that the accuracy rate of the model's judgment comments is around 71.08%, which is more accurate and stable. However, there are still some issues when judging comments with emojis and emoticons, and certain data is needed to support the judgment of comments for different movies or dramas. There are also certain issues with the dataset, as the data is manually annotated, and the annotation of the dataset itself may also be influenced by the annotator, which may lead to inaccurate judgments.
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Bao, Yuchen, Hongyi Huang i Zizhou Meng. "Sentiment analysis based on BiLSTM with attention mechanism on Chinese comment with stickers". Applied and Computational Engineering 38, nr 1 (22.01.2024): 26–34. http://dx.doi.org/10.54254/2755-2721/38/20230525.
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As the Internet is progressively becoming larger and more intricate, more and more users of various social media choose to post their comments to express their opinions and thinking on those platforms. Analyzing the emotions contained in user comments holds great business value, helping to accurately perceive user consumption habits and improve user service levels. However, the use of emoticons and stickers in comments has increased dramatically in recent years, which brings new challenges to text sentiment analysis based on natural language processing. In this paper, in order to alleviate the above problems, we propose a method for analyzing the sentiment of Chinese comments based on the attention mechanism and BiLSTM. Specifically, we partitioned the original dataset from the Weibo platform according to the number and type of emoticons in the comments. By analyzing the actual data, the specific features of emojis that affect the performance of sentiment analysis are identified, and corresponding explanations are given. In addition, a hypothesis is proposed to quantify the impact of emoticons on model effectiveness. All the results demonstrate the effectiveness of our proposed method.
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Beseiso, Majdi. "Word and Character Information Aware Neural Model for Emotional Analysis". Recent Patents on Computer Science 12, nr 2 (25.02.2019): 142–47. http://dx.doi.org/10.2174/2213275911666181119112645.
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Background: Social media texts are often highly unstructured in accordance with the presence of hashtags, emojis and URLs occurring in abundance. Thus, a sentiment or emotion analysis on these kinds of texts becomes very difficult. The difficulty increases even more when such texts are in local languages like Arabic. Methods: This work utilizes novel deep learning architectures in the form of character-level Convolutional Neural Network (CNN) module and the word-level Recurrent Neural Network (RNN) module to produce a hybrid architecture that makes use of the character level analysis and the word level analysis to obtain state-of-the-art results on a totally new Arabic Emotions dataset. Results: The proposed method works the best among the traditional bag-of-words and Term Frequency and Inverse Document Frequency methods for emotion analysis. It also outperforms the state-of-the-art deep learning methods which are known to perform very well in an English corpus. Conclusion: The proposed deep end-to-end architecture utilizes the character level information from a text through the Character CNN Module and the word level information from a text through the Word-Level RNN Module.
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Mazza Zago, Ricardo, i Luciane Agnoletti dos Santos Pedotti. "BERTugues: A Novel BERT Transformer Model Pre-trained for Brazilian Portuguese". Semina: Ciências Exatas e Tecnológicas 45 (20.12.2024): e50630. https://doi.org/10.5433/1679-0375.2024.v45.50630.
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Large Language Models (LLMs) are trained for English or multilingual versions, with superior performance in English. This disparity occurs because, in the training of multilingual models, only a relatively small amount of data is added for each additional language. Consequently, while these models can function in Portuguese, their performance is suboptimal. The first BERT model (Bidirectional Encoder Representations from Transformers) specifically trained for Brazilian Portuguese was BERTimbau in 2020, which enhanced performance across various text-related tasks. We followed the training approach of BERT/BERTimbau for BERTugues, while implementing several improvements. These included removing rarely used characters in Portuguese from the tokenizer, such as oriental characters, resulting in the addition of over 7,000 new tokens. As a result, the average length of sentence representations was reduced from 3.8 words with more than one token to 3.0, which positively impacted embedding performance by improving metrics relevant to classification problems. Two additional enhancements involved embedding emojis as tokens - an essential step for capturing conversational nuances - and filtering low-quality texts from the training dataset. These modifications improved performance across various tasks, raising the average F1 score from 64.8 % in BERTimbau to 67.9 % in BERTugues.
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Olaniyan, Deborah, Roseline Oluwaseun Ogundokun, Olorunfemi Paul Bernard, Julius Olaniyan, Rytis Maskeliūnas i Hakeem Babalola Akande. "Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media". Computers 12, nr 11 (14.11.2023): 231. http://dx.doi.org/10.3390/computers12110231.
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Sarcasm and irony represent intricate linguistic forms in social media communication, demanding nuanced comprehension of context and tone. In this study, we propose an advanced natural language processing methodology utilizing long short-term memory with an attention mechanism (LSTM-AM) to achieve an impressive accuracy of 99.86% in detecting and interpreting sarcasm and irony within social media text. Our approach involves innovating novel deep learning models adept at capturing subtle cues, contextual dependencies, and sentiment shifts inherent in sarcastic or ironic statements. Furthermore, we explore the potential of transfer learning from extensive language models and integrating multimodal information, such as emojis and images, to heighten the precision of sarcasm and irony detection. Rigorous evaluation against benchmark datasets and real-world social media content showcases the efficacy of our proposed models. The outcomes of this research hold paramount significance, offering a substantial advancement in comprehending intricate language nuances in digital communication. These findings carry profound implications for sentiment analysis, opinion mining, and an enhanced understanding of social media dynamics.
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Kumar, K. Dileep. "Multilingual Hate Speech Detection Using NLP Techniques". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, nr 03 (4.03.2025): 1–9. https://doi.org/10.55041/ijsrem42063.
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- Hate speech detection in multiple languages has emerged as a significant challenge in Natural Language Processing (NLP), primarily due to the diverse linguistic structures, cultural nuances, and variations in contextual meanings across languages. Unlike monolingual hate speech detection, which relies on well-established lexicons and training datasets, multilingual detection requires sophisticated models capable of handling code-switching, dialectal variations, and the absence of extensive labeled data for many languages. We explore various NLP techniques, including machine learning models, deep learning architectures, and transformer-based approaches for detecting hate speech across different languages. A critical aspect of hate speech detection is text preprocessing, which varies depending on the language. The preprocessing techniques such as tokenization, stopword removal, stemming, lemmatization, and handling emojis, slang, and abbreviations commonly found in online discourse. Additionally, we examine feature engineering methods, including Term Frequency-Inverse Document Frequency (TF-IDF), word embeddings (Word2Vec, GloVe, FastText), and contextual embeddings generated by transformer models. Key Words: Hate speech detection, NLP, multilingual, machine learning, deep learning, tokenization, stopword removal, stemming, lemmatization
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Wylie, Michelle. "Culture and paralinguistic features ~!^^:-): East meets West in a virtual exchange between South Korea and England". Journal of Virtual Exchange 3 (SI-IVEC2019) (2.12.2020): 49–67. http://dx.doi.org/10.21827/jve.3.35807.
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This paper investigates whether cultural differences are apparent in the paralinguistic features used by culturally diverse interactants online. Paralinguistic features are used pervasively in digital discourse (Herring & Androutsopoulos, 2015), therefore they play a pivotal role in online communication skills. Paralinguistic features such as the innovative use of punctuation and typographical features as well as emoticons and emojis are used to add nuance, emotional tone, and to manage discourse in online communication. However, the effectiveness of these paralinguistic features is dependent upon a shared understanding of their functions. This study seeks to explore any potential cultural manifestations in the use of paralinguistic features during a semester-long virtual exchange between 21 South Korean students and 25 students studying at a university in England. The dataset of 20,379 words generated during the virtual exchange was examined for cultural manifestations in paralinguistic features. As this study examines potential cultural manifestations online, it adheres to a culturally relativist perspective, therefore an inductive approach to the analysis of the data was taken. The analysis of the data revealed culturally specific paralinguistic features with the emergence of a feature that, to the best of my knowledge, has not been recorded in previous virtual exchange research.
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Bavkar, Dnyaneshwar Madhukar, Ramgopal Kashyap i Vaishali Khairnar. "Multimodal Sarcasm Detection via Hybrid Classifier with Optimistic Logic". Journal of Telecommunications and Information Technology 3, nr 2022 (29.09.2022): 97–114. http://dx.doi.org/10.26636/jtit.2022.161622.
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This work aims to provide a novel multimodal sarcasm detection model that includes four stages: pre-processing, feature extraction, feature level fusion, and classification. The pre-processing uses multimodal data that includes text, video, and audio. Here, text is pre-processed using tokenization and stemming, video is pre-processed during the face detection phase, and audio is pre-processed using the filtering technique. During the feature extraction stage, such text features as TF-IDF, improved bag of visual words, n-gram, and emojis as well on the video features using improved SLBT, and constraint local model (CLM) are extraction. Similarly the audio features like MFCC, chroma, spectral features, and jitter are extracted. Then, the extracted features are transferred to the feature level fusion stage, wherein an improved multilevel canonical correlation analysis (CCA) fusion technique is performed. The classification is performer using a hybrid classifier (HC), e.g. bidirectional gated recurrent unit (Bi-GRU) and LSTM. The outcomes of Bi-GRU and LSTM are averaged to obtain an effective output. To make the detection results more accurate, the weight of LSTM will be optimally tuned by the proposed opposition learning-based aquila optimization (OLAO) model. The MUStARD dataset is a multimodal video corpus used for automated sarcasm Discovery studies. Finally, the effectiveness of the proposed approach is proved based on various metrics.
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Agarkar, Priyanshu T., Pranav Chopdekar, Sahil Gujar i Komal Chitnis. "SenseWorth – A Tweets Classifier". International Journal for Research in Applied Science and Engineering Technology 10, nr 12 (31.12.2022): 1040–48. http://dx.doi.org/10.22214/ijraset.2022.48056.
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Abstract: A lot of people use tweeter to provide their opinions on various topics including sports, politics, finance, etc. Now the information that generally is present on Twitter does not have any means to check whether the data being twitter is correct or not. So in order to check the authenticity of the data,the data must be classified into true and false. Firstly, the dataset would be created using python by devising the code for the same. The code thus designed would be able to extract the tweets with the amount specified by the user. Hence the CSV would be created. After the creation of the CSV, data pre-processing would be applied to the data such that all the unnecessary data such as emojis, words, and information would be removed automatically, and thus we would receive the tweet without any kind of stop-words. This cleaned data would further be tested and trained with the help of machine learning algorithms. These Machine learning algorithms would generally be used to classify the data according to the domain and provide the user with an authenticated answer whether the tweet is true or not along with its accuracy. This helpsthe userto identify the tweet and thus provide an authenticated answer on which informationto believe and to which we should not
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XU, CAIMING, SILEI SUI, Keisuke Okuno, Silvia Pascual-Sabater, Cristina Fillat i Ajay Goel. "Abstract 3821: Berberine and emodin synergistically suppress the EGFR signaling cascade by targeting LAMB3 in pancreatic ductal adenocarcinoma". Cancer Research 83, nr 7_Supplement (4.04.2023): 3821. http://dx.doi.org/10.1158/1538-7445.am2023-3821.
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Abstract Background: Pancreatic cancer is one of the most devastating malignancies due to the development of intrinsic chemoresistance following chemotherapy. Extracellular matrix (ECM) proteins are intimately linked to cellular proliferation, invasion, and acquisition of chemoresistance in PDAC cells, making them promising therapeutic targets in this malignancy. Naturally occurring dietary botanicals, including berberine (BER) and emodin (EMO), have been shown to suppress ECM as one of the mechanism(s) for their anti-tumorigenic activity, along with their time-tested safety and cost-effectiveness. In addition, both BER and EMO are also known to induce apoptosis by modulating different pathways and regulating pro-apoptotic genes. Herein, we hypothesized that combined treatment with BER and EMO might exhibit synergistic anticancer efficacy by targeting the ECM and apoptotic pathways in PDAC cells. Methods: We undertook genomewide transcriptomic profiling analysis to identify critical ECM-related genes differentially expressed in PDAC. Subsequently, the TCGA dataset was analyzed to identify the prognostic significance of ECM-associated genes with overall survival (OS) and disease-free survival (DFS) in PDAC. A series of cell culture experiments were performed using PDAC cells, followed by their validation in patient-derived organoids to examine the synergistic anti-proliferative and chemopreventive effects of BER and EMO against PDAC. Results: Transcriptomic profiling identified that LAMB3 expression was significantly upregulated in PDAC tissue (P < 0.01) and was significantly associated with poor OS and DFS in PDAC patients (P < 0.01). The combination of BER and EMO displayed superior synergistic anti-tumor potential in PDAC cells vs. individual compounds, as revealed by cell proliferation, clonogenicity, migration, and invasion assays. The combination of BER and EMO also altered the expression of key proteins involved in cellular apoptosis, epithelial-mesenchymal-transition, and EGFR/ERK//AKT growth factor signaling pathways. Finally, these findings were successfully validated in PDAC patient-derived 3D organoids. Conclusions: We provide the first evidence that combined treatment with berberine and emodin exerts synergistic anti-cancer activity in PDAC, primarily regulated through the LAMB3-mediated interaction with other members of the EGFR-signaling pathway. Citation Format: CAIMING XU, SILEI SUI, Keisuke Okuno, Silvia Pascual-Sabater, Cristina Fillat, Ajay Goel. Berberine and emodin synergistically suppress the EGFR signaling cascade by targeting LAMB3 in pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3821.
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Yang, Tao, Ziyu Liu, Yu Lu i Jun Zhang. "Centrifugal Navigation-Based Emotion Computation Framework of Bilingual Short Texts with Emoji Symbols". Electronics 12, nr 15 (3.08.2023): 3332. http://dx.doi.org/10.3390/electronics12153332.
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Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy of tendency prediction and score calculation. For these problems, this paper proposes a Centrifugal Navigation-Based Emotional Computation framework (CNEC). CNEC adopts Emotional Orientation of Related Words (EORW) to calculate scores of unknown Chinese/English words and emoji symbols. In EORW, t neighbor words of the predicted sample from one element in the short text are selected from a sentiment dictionary according to spatial distance, and related words are extracted using the emotional dominance principle from the t neighbor words. Emotional scores of related words are fused to calculate scores of the predicted sample. Furthermore, CNEC utilizes Centrifugal Navigation-Based Emotional Fusion (CNEF) to achieve the emotional fusion of heterogeneous corpora. In CNEF, how the emotional fluctuation occurs is illustrated by the trigger angle of centrifugal motion in physical theory. In light of the corresponding relationship between the trigger angle and conditions of the emotional fluctuation, the fluctuation position is determined. Lastly, emotional fusion with emotional fluctuation is carried out by a CNEF function, which considers the fluctuation position as a significant position. Experiments demonstrate that the proposed CNEC effectively computes emotional scores for bilingual short texts with emojis on the Weibo dataset collected.