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Journal articles on the topic 'Text to emotion'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Коlyadko, S. V. "PLOT, STORY, COMPOSITION IN THE EMOTIVE TEXT OF POETRY." Proceedings of the National Academy of Sciences of Belarus, Humanitarian Series 63, no. 3 (2018): 355–65. http://dx.doi.org/10.29235/2524-2369-2018-63-3-355-365.

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In the article there is an attempt to enter emotion in the structure of work, namely to look at its action at the level of plot and composition. It becomes firmly established that an emotion is just the way to create a plot. The poetic work consists of emotional events, each of which has its own dominant emotion. Motion of these emotions forms composition of a plot. A thesis is grounded that emotional events, incorporated by a general emotion, express certain emotive topics that predetermine as a whole the development of a plot. Emotions are a part of poetic text, emotions determine its emotio

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Zhang, Ziheng. "Review of text emotion detection." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 213–21. http://dx.doi.org/10.54097/hset.v12i.1456.

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Emotion is one of the essential characteristics of being human. When writing essays or reports, people will add their own emotions. Text sentiment detection can detect the leading emotional tone of a text. Text emotion detection and recognition is a new research field related to sentiment analysis. Emotion analysis detects and identifies emotion types, such as anger, happiness, or sadness, through textual expression. It is a subdomain of NLP. For some applications, the technology could help large companies' Chinese and Russian data analysts gauge public opinion or conduct nuanced market resear

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Thamaraiselvi , Dr D., J. Pranay, and S. Hruthik Kasyap. "Emotion Detection from Video and Audio and Text." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40494.

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Emotion detection from video, audio, and text has emerged as a vital area of research within the fields of artificial intelligence and human-computer interaction. As digital communication increasingly integrates multiple modalities, understanding human emotions through these various channels has become essential for enhancing user experience, improving mental health diagnostics, and advancing affective computing technologies. This paper presents a comprehensive overview of the methodologies and frameworks developed for detecting emotions from video, audio, and text inputs, highlighting the syn

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Ptaszynski, Michal, Jacek Maciejewski, Pawel Dybala, Rafal Rzepka, and Kenji Araki. "CAO: A Fully Automatic Emoticon Analysis System." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1026–32. http://dx.doi.org/10.1609/aaai.v24i1.7715.

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This paper presents CAO, a system for affect analysis of emoticons. Emoticons are strings of symbols widely used in text-based online communication to convey emotions. It extracts emoticons from input and determines specific emotions they express. Firstly, by matching the extracted emoticons to a raw emoticon database, containing over ten thousand emoticon samples extracted from the Web and annotated automatically. The emoticons for which emotion types could not be determined using only this database, are automatically divided into semantic areas representing "mouths" or "eyes," based on the t

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Fujisawa, Akira, Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita. "Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences." Applied Sciences 12, no. 3 (2022): 1256. http://dx.doi.org/10.3390/app12031256.

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This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features

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Mullangi, Pradeep, Nagajyothi Dimmita, M. Supriya, et al. "Sentiment and Emotion Modeling in Text-based Conversations utilizing ChatGPT." Engineering, Technology & Applied Science Research 15, no. 1 (2025): 20042–48. https://doi.org/10.48084/etasr.9508.

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Emotional Intelligence (EI) constitutes a vital element of human communication, and its integration into text-based dialogues has gained great significance in the modern digital era. The present paper proposes an innovative method for modeling sentiment and emotion within text-based conversations using the ChatGPT language model. The advancements in sentiment and emotion recognition are centered on the role of EI in text-based conversational models. The study underscores the significance of diverse datasets, including Interactive Emotional Dyadic Motion Capture (IEMOCAP), MELD, EMORYNLP, and D

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Sundar, Balapuri Shiva. "Emotion Detection on text using Machine Learning and Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2277–86. http://dx.doi.org/10.22214/ijraset.2022.44293.

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Abstract: Emotion detection on text is an important field of research in Artificial Intelligence and human-computer interaction. Emotions play key role in human interaction. Emotion detection is closely associated with sentiment detection, in which we detect the polarity of the text. But in emotion detection, we detect emotions such as joy, love, surprise, sadness, fear, and anger. Emotion detection helps the machines to understand human behavior and ultimately it provides users with emotional awareness feedback. In this paper, we are going to compare Machine Learning and Deep Learning techniq

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Boutouta, Hanane, Abdelaziz Lakhfif, Ferial Senator, and Chahrazed Mediani. "A Transformer-based Hybrid Model for Implicit Emotion Recognition in Arabic Text." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23834–39. https://doi.org/10.48084/etasr.10261.

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Implicit emotion recognition has emerged as an active area of research in modern Natural Language Processing (NLP). Unlike explicit emotions, which are directly expressed through emotional words, implicit emotions are inferred from the surrounding context, making their detection more challenging. While most research in Arabic NLP has focused on recognizing explicit emotions, the study of implicit emotions remains largely unexplored, primarily due to its unique linguistic and morphological characteristics. The current study addresses this gap by compiling an Arabic dataset for the implicit emot

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Putri, Dewi Melisa, and Yulian Findawati. "Text Preprocessing on Emotional Tweets Case Study: Covid-19 Vaccine Rejection." Procedia of Engineering and Life Science 7 (March 14, 2024): 446–53. http://dx.doi.org/10.21070/pels.v7i0.1501.

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Emotion is a feeling, response, reaction to an event or event experienced by a person, can be expressed verbally (spoken) or non-verbally (body language). In the digital world, more precisely text-based social media, humans express their emotions by giving opinions through text / typing and there is a need for emotional analysis related to what opinions are conveyed by that person. Therefore, to overcome the above problems, it is necessary to classify to find out what type of emotion is conveyed in the text. Emotion classification is a technique to extract information in the form of a person's

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Cahyani, Denis Eka, and Irene Patasik. "Performance comparison of TF-IDF and Word2Vec models for emotion text classification." Bulletin of Electrical Engineering and Informatics 10, no. 5 (2021): 2780–88. http://dx.doi.org/10.11591/eei.v10i5.3157.

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Emotion is the human feeling when communicating with other humans or reaction to everyday events. Emotion classification is needed to recognize human emotions from text. This study compare the performance of the TF-IDF and Word2Vec models to represent features in the emotional text classification. We use the support vector machine (SVM) and Multinomial Naïve Bayes (MNB) methods for classification of emotional text on commuter line and transjakarta tweet data. The emotion classification in this study has two steps. The first step classifies data that contain emotion or no emotion. The second st

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Liu, Changxiu, S. Kirubakaran, and Alfred Daniel J. "Deep Learning Approach for Emotion Recognition Analysis in Text Streams." International Journal of Technology and Human Interaction 18, no. 2 (2022): 1–21. http://dx.doi.org/10.4018/ijthi.313927.

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Social media sites employ various approaches to track feelings, including diagnosing neurological problems, including fear, in people or assessing a population public sentiment. One essential obstacle for automatic emotion recognition principles is variable with fluctuating limitations, language, and interpretation shifts. Therefore, in this paper, a deep learning-based emotion recognition (DL-EM) system has been proposed to describe the various relational effects in emotional groups. A soft classification method is suggested to quantify the tendency and allocate a message to each emotional cl

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Naik, Anil S. "Text and Voice Based Emotion Monitoring System." Oriental journal of computer science and technology 12, no. 4 (2020): 194–200. http://dx.doi.org/10.13005/ojcst12.04.05.

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An Emotion monitoring system for a call-center is proposed. It aims to simplify the tracking and management of emotions extracted from call center Employee-Customer conversations. The system is composed of four modules: Emotion Detection, Emotion Analysis and Report Generation, Database Manager, and User Interface. The Emotion Detection module uses Tone Analyzer to extract them for reliable emotion; it also performs the Utterance Analysis for detecting emotion. The 14 emotions detected by the tone analyzer are happy, joy, anger, sad and neutral, etc. The Emotion Analysis module performs classi

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Deng, Jiawen, and Fuji Ren. "Hierarchical Network with Label Embedding for Contextual Emotion Recognition." Research 2021 (January 6, 2021): 1–9. http://dx.doi.org/10.34133/2021/3067943.

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Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recogniti

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Bauer, Karen. "Emotion in the Qur'an: An Overview." Journal of Qur'anic Studies 19, no. 2 (2017): 1–30. http://dx.doi.org/10.3366/jqs.2017.0282.

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In the Western academic study of the Qur'an, very little has been written about emotion. The studies that do acknowledge the power of emotion tend to concentrate on emotion as a response to the text's aesthetics. And yet emotion is a central part of the Qur'an: fostering the correct emotions is a part of pietistic practice, emotion helps to convince believers to act as they should, and emotional words and incidents bring unity to this synoptic text. This article has four parts. It begins by reviewing approaches that have been taken in History and Biblical studies, in order to clarify the natur

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Franza, Jasmin, Bojan Evkoski, and Darja Fišer. "Emotion analysis in socially unacceptable discourse." Slovenščina 2.0: empirical, applied and interdisciplinary research 10, no. 1 (2022): 1–22. http://dx.doi.org/10.4312/slo2.0.2022.1.1-22.

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Texts often express the writer’s emotional state, and it was shown that emotion information has potential for hate speech detection and analysis. In this work, we present a methodology for quantitative analysis of emotion in text. We define a simple, yet effective metric for an overall emotional charge of text based on the NRC Emotion Lexicon and Plutchik’s eight basic emotions. Using this methodology, we investigate the emotional charge of content with socially unacceptable discourse (SUD), as a distinct and potentially harmful type of text which is spreading on social media. We experiment wi

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Sukhanova, Irina. "Emotional Potential of Non-Emotive Vocabulary in the Modern English Literary Text." Izvestia of Smolensk State University, no. 2(58) (July 3, 2022): 118–29. http://dx.doi.org/10.35785/2072-9464-2022-58-2-118-129.

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The study is devoted to the features of lexicalization of the basic emotion «fear» in an English literary text in order to determine the emotive potential of non-emotive vocabulary representing this emotion. The use of cognitive approaches to the study of the language, the internal structure in the naming of emotions, the ways of their objectification and lexicalization reflect the relevance of the study. The analysis of the semantic structure of the emotion «fear» in the Russian and English languages as well as the linguistic representation of the basic emotion «fear» in the texts

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J.A.D.P.R, De Silva, Lanka P.A.C, Jayawardena R.D.T.M, and Nandakumara K.S.S. "EMOSENSE – Multi-Modal Emotion Recognition to Identify Emotions." International Research Journal of Innovations in Engineering and Technology 07, no. 10 (2023): 428–36. http://dx.doi.org/10.47001/irjiet/2023.710057.

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An extended study has been done over the past years to better comprehend human emotions. The embracement of technology to recognize and react to human emotions has become a required component of society. We present a fully functional multi-modal emotion recognition system in this study that integrates data from text, voice, facial expressions, and body language. In this study, the automatic classification of anger, fear, joy, sadness, surprise, disgust, and neutral emotions from text, facial expressions, voice, and body movements have been studied on the TESS, MELD, FER2013, and EDNLP datasets

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Szabóová, Martina, Martin Sarnovský, Viera Maslej Krešňáková, and Kristína Machová. "Emotion Analysis in Human–Robot Interaction." Electronics 9, no. 11 (2020): 1761. http://dx.doi.org/10.3390/electronics9111761.

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This paper connects two large research areas, namely sentiment analysis and human–robot interaction. Emotion analysis, as a subfield of sentiment analysis, explores text data and, based on the characteristics of the text and generally known emotional models, evaluates what emotion is presented in it. The analysis of emotions in the human–robot interaction aims to evaluate the emotional state of the human being and on this basis to decide how the robot should adapt its behavior to the human being. There are several approaches and algorithms to detect emotions in the text data. We decided to app

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Ranjan, Nihar M. "Text to Speech Conversion based on Emotion using Recurrent Neural Network." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 970–78. http://dx.doi.org/10.22214/ijraset.2023.55207.

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Abstract: Emotion based text to speech conversion system will be proved as an improved version of traditional text to speech system. Emotions help us in recognizing the message conveyed by the conveyor in more effective way. ETTS (Emotion based text to speech conversion system) will stand out from TTS (Text to speech) as it will have variation of voice according to the emotions detected in the text. ETTS will detect four basic and most used emotions of humans. These emotions are ‘happy’, ‘sad’, ‘anger’, ‘neutral’. As ETTS will use RNN (Recurrent Neural Network) foridentifying emotions, emotion

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Ranjan, Nihar M. "Text to Speech Conversion based on Emotion using Recurrent Neural Network." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 970–78. http://dx.doi.org/10.22214/ijraset.2023.55207.

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Abstract: Emotion based text to speech conversion system will be proved as an improved version of traditional text to speech system. Emotions help us in recognizing the message conveyed by the conveyor in more effective way. ETTS (Emotion based text to speech conversion system) will stand out from TTS (Text to speech) as it will have variation of voice according to the emotions detected in the text. ETTS will detect four basic and most used emotions of humans. These emotions are ‘happy’, ‘sad’, ‘anger’, ‘neutral’. As ETTS will use RNN (Recurrent Neural Network) foridentifying emotions, emotion

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Graterol, Wilfredo, Jose Diaz-Amado, Yudith Cardinale, Irvin Dongo, Edmundo Lopes-Silva, and Cleia Santos-Libarino. "Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology." Sensors 21, no. 4 (2021): 1322. http://dx.doi.org/10.3390/s21041322.

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For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this inf

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Li, Feng, Yintong Huo, and Lingling Wang. "Research on Chinese Emotion Classification using BERT-RCNN-ATT." WSEAS TRANSACTIONS ON COMMUNICATIONS 22 (March 17, 2023): 17–31. http://dx.doi.org/10.37394/23204.2023.22.2.

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Emotional classification is the process of analyzing and reasoning subjective texts with emotional color, that is, analyzing whether their emotional tendencies are positive or negative. Aiming at the problems of massive data and nonstandard words in the existing Chinese short text emotion classification algorithm, the traditional BERT model does not distinguish the semantics of words with the same sentence pattern clearly, the multi-level transformer training is slow, time-consuming, and requires high energy consumption, this paper proposes to classify users' emotions based on BERT-RCNN-ATT mo

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Khan, Shahidul Islam, FaisalBinAziz, and MdMisbah Uddin. "Emotion Detection from Multilingual Text and Multi-Emotional Sentence using Difference NLP Feature Extraction Technique and ML Classifier." International Journal of Advanced Networking and Applications 14, no. 03 (2022): 5429–35. http://dx.doi.org/10.35444/ijana.2022.14303.

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Machines can read, comprehend, and extrapolate meaning from human languages, thanks to natural language processing.In this paper, we have detected emotion from multilingual text and multi-emotional sentences.For our research, we have collected a dataset containing around 7000 tweets on 4 emotions (Anger, Fear, Joy, and Sadness). After pre-processing our data, we used 2 NLP feature extraction models and trained those with the help of 4 different Machine Learning classifiers. We have also developed an algorithm for detecting exact emotions from multi-emotional sentences. Also, we compared our re

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Kim, Eun Hee, and Ju Hyun Shin. "Multi-Modal Emotion Recognition in Videos Based on Pre-Trained Models." Korean Institute of Smart Media 13, no. 10 (2024): 19–27. http://dx.doi.org/10.30693/smj.2024.13.10.19.

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Recently, as the demand for non-face-to-face counseling has rapidly increased, the need for emotion recognition technology that combines various aspects such as text, voice, and facial expressions is being emphasized. In this paper, we address issues such as the dominance of non-Korean data and the imbalance of emotion labels in existing datasets like FER-2013, CK+, and AFEW by using Korean video data. We propose methods to enhance multimodal emotion recognition performance in videos by integrating the strengths of image modality with text modality. A pre-trained model is used to overcome the

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Sun, Yawei, Saike He, Xu Han, and Ruihua Zhang. "A New Model for Emotion-Driven Behavior Extraction from Text." Applied Sciences 13, no. 15 (2023): 8700. http://dx.doi.org/10.3390/app13158700.

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Emotion analysis is currently a popular research direction in the field of natural language processing. However, existing research focuses primarily on tasks such as emotion classification, emotion extraction, and emotion cause analysis, while there are few investigations into the relationship between emotions and their impacts. To address these limitations, this paper introduces the emotion-driven behavior extraction (EDBE) task, which addresses these limitations by separately extracting emotions and behaviors to filter emotion-driven behaviors described in text. EDBE comprises three sub-task

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Muhammad, Anwarul Azim, and Hasan Bhuiyan Mahmudul. "Text to Emotion Extraction Using Supervised Machine Learning Techniques." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1394–401. https://doi.org/10.12928/TELKOMNIKA.v16i3.8387.

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Proliferation of internet and social media has greatly increased the popularity of text communication. People convey their sentiment and emotion through text which promotes lively communication. Consequently, a tremendous amount of emotional text is generated on different social media and blogs in every moment. This has raised the necessity of automated tool for emotion mining from text. There are various rule based approaches of emotion extraction form text based on emotion intensity lexicon. However, creating emotion intensity lexicon is a time consuming and tedious process. Moreover, there

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Supriya, Dhanaraj Dhumale, Vitthal Khopade Manjiri, Dhimate Bhushan, and Yogesh Dhere Avadhoot. "A Brief Survey on Emotion Based Text to Speech Conversion System." International Journal of Soft Computing and Engineering (IJSCE) 11, no. 1 (2021): 40–43. https://doi.org/10.35940/ijsce.A3529.0911121.

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Text to speech conversion is one of the applications of machine learning. It is widely used in search engines, standalone applications, web applications, chatbots and android applications. But still there is need to upgrade text to speech system so that we can get more interactive and user-friendly application. Traditional text to speech application has monotonous voice as output which does not has emotions in it and seems to be more mechanized. So, there is need to improvise the existing system by embedding the flavour of emotions in it. Existing text to speech cannot be used in story telling

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Li, Chen, and Fanfan Li. "Emotion recognition of social media users based on deep learning." PeerJ Computer Science 9 (June 14, 2023): e1414. http://dx.doi.org/10.7717/peerj-cs.1414.

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Issues with sentiment analysis in social media include neglecting the long-distance semantic link of emotional features, failing to capture the feature words with emotional hue effectively, and depending excessively on manual annotation. This research provides a user emotion recognition model to achieve the emotional analysis of microblog public opinion events. Three types of inspiring text, “joy,” “anger,” and “sadness,” are obtained by the data collecting and data preprocessing of micro-blog public opinion event comment text. Then, an algorithm using the linear discriminant analysis (LDA) mo

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Fathy, Samar, Nahla El-Haggar, and Mohamed H. Haggag. "A Hybrid Model for Emotion Detection from Text." International Journal of Information Retrieval Research 7, no. 1 (2017): 32–48. http://dx.doi.org/10.4018/ijirr.2017010103.

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Emotions can be judged by a combination of cues such as speech facial expressions and actions. Emotions are also articulated by text. This paper shows a new hybrid model for detecting emotion from text which depends on ontology with keywords semantic similarity. The text labelled with one of the six basic Ekman emotion categories. The main idea is to extract ontology from input sentences and match it with the ontology base which created from simple ontologies and the emotion of each ontology. The ontology extracted from the input sentence by using a triplet (subject, predicate, and object) ext

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Syafiqah, Annisa, Syahiduz Zaman, and Mochamad Imamudin. "Text Mining Approach to Emotion Analysis in Translation of Surah Yusuf With NRC Emotion Lexicon." IT Journal Research and Development 9, no. 2 (2025): 108–22. https://doi.org/10.25299/itjrd.2025.17765.

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In the digital era, the accessibility of vast textual data, including the Quran, has facilitated broader comprehension of its teachings. This study analyzes the emotions in the English translation of Surah Yusuf using the NRC Emotion Lexicon. The findings show that trust is the most dominant emotion (22.89%), followed by joy (15.66%), anticipation (13.25%), sadness (12.05%), fear (10.84%), anger (9.64%), surprise (8.43%), and disgust (7.23%). These results confirm the text's diverse emotional expressions and the effectiveness of the lexicon-based method. The research aligns with the initial go

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Sahoo, Sipra. "Emotion Recognition from Text." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (2018): 237–43. http://dx.doi.org/10.22214/ijraset.2018.3038.

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Ramalingam, V. V., A. Pandian, Abhijeet Jaiswal, and Nikhar Bhatia. "Emotion detection from text." Journal of Physics: Conference Series 1000 (April 2018): 012027. http://dx.doi.org/10.1088/1742-6596/1000/1/012027.

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Bezrukov, Andrii, and Oksana Bohovyk. "Emotion concepts for representing the vicissitudes of fate in Markus Zusak’s “Bridge of Clay”." Synopsis: Text Context Media 28, no. 1 (2022): 14–20. http://dx.doi.org/10.28925/2311-259x.2022.1.3.

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The problem of studying emotionally expressive information contained in a text is of considerable interest since it interprets reality, expressing value or emotionally significant attitudes toward this reality. The analysis of the emotivity and expressiveness of a literary text focuses primarily on its research from the cognitive (separation of emotiogenic knowledge) and semantic (determining the features of its use to indicate the author’s purposes) perspectives. A literary text is considered as a dual dimension: on the one hand, it is related to emotions, and on the other hand, it is specifi

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Chandurkar, Swati S., Shailaja V. Pede, and Shailesh A. Chandurkar. "System for Prediction of Human Emotions and Depression level with Recommendation of Suitable Therapy." Asian Journal of Computer Science and Technology 6, no. 2 (2017): 5–12. http://dx.doi.org/10.51983/ajcst-2017.6.2.1787.

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In today’s competitive world, an individual needs to act smartly and take rapid steps to make his place in the competition. The ratio of the youngsters to that of the elder people is comparatively more and also they contribute towards the development of the society. This paper presents the methodology to extract emotion from the text at real time and add the expression to the textual contents during speech synthesis by using Corpus , emotion recognition module etc. Along with the emotions recognition from the human textual data the system will analyze the various human body signals such as blo

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Yi, Moung-Ho, Keun-Chang Kwak, and Ju-Hyun Shin. "KoHMT: A Multimodal Emotion Recognition Model Integrating KoELECTRA, HuBERT with Multimodal Transformer." Electronics 13, no. 23 (2024): 4674. http://dx.doi.org/10.3390/electronics13234674.

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With the advancement of human-computer interaction, the role of emotion recognition has become increasingly significant. Emotion recognition technology provides practical benefits across various industries, including user experience enhancement, education, and organizational productivity. For instance, in educational settings, it enables real-time understanding of students’ emotional states, facilitating tailored feedback. In workplaces, monitoring employees’ emotions can contribute to improved job performance and satisfaction. Recently, emotion recognition has also gained attention in media a

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Walke, Shubham. "Classification of poetry text into the emotional state using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41275.

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Poetry can elicit powerful emotions on the basis of elevated language and literary devices, and placing these emotions becomes problematic owing to the subjectivity of human emotions and the complexity entwined in poetic expression. This research puts forward a deep learning-based approach towards automatic classification of poetry into emotions like happiness, sadness, anger, fear, and surprise. The proposed approach also engages advanced NLP techniques that aid in discovering the semantics and emotional hinterlands of poems. For the purpose of this study, we represent poetic texts in higher-

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Hung, Lai Po, and Suraya Alias. "Beyond Sentiment Analysis: A Review of Recent Trends in Text Based Sentiment Analysis and Emotion Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 1 (2023): 84–95. http://dx.doi.org/10.20965/jaciii.2023.p0084.

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Sentiment Analysis is probably one of the best-known area in text mining. However, in recent years, as big data rose in popularity more areas of text classification are being explored. Perhaps the next task to catch on is emotion detection, the task of identifying emotions. This is because emotions are the finer grained information which could be extracted from opinions. So besides writer sentiments, writer emotion is also a valuable data. Emotion detection can be done using text, facial expressions, verbal communications and brain waves; however, the focus of this review is on text-based sent

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Li, Weiyuan, and Hua Xu. "Text-based emotion classification using emotion cause extraction." Expert Systems with Applications 41, no. 4 (2014): 1742–49. http://dx.doi.org/10.1016/j.eswa.2013.08.073.

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Huang, Yuxin. "Research on Lovelorn Emotion Recognition Based on Ernie Tiny." Frontiers in Computing and Intelligent Systems 2, no. 2 (2023): 66–69. http://dx.doi.org/10.54097/fcis.v2i2.4145.

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Topics related to sentiment classification and emotion recognition are an important part of the Natural Language Processing research field and can be used to analyze users' sentiment tendencies towards brands, understand the public's attitudes and opinions on public opinion events, and detect users' mental health, among others. Past research has usually been based on positive and negative emotions or multi-categorized emotions such as happiness, anger and sadness, while there has been little research on the recognition of the specific emotion of lovelorn. This study aims to identify the lovelo

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N, Tejashwini, V. Kaveri A, Keerthana P, Rajneesh Kumar, and M. Kavya C. "Multimodal Emotion Recognition." Perspectives in Communication, Embedded-systems and Signal-processing - PiCES 4, no. 8 (2020): 194–98. https://doi.org/10.5281/zenodo.4419690.

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Recognizing different emotions of humans for system has been a burning issue since last decade. The association between individuals and PCs will be increasingly normal if PCs can see and react to human non-verbal correspondence, for example, feelings. Albeit a few methodologies have been proposed to perceive human feelings dependent on outward appearances or discourse or text, generally restricted work has been three models and other modalities to improve the capacities of the feeling acknowledgment framework. This paper describes the qualities and the restrictions of frameworks dependent on o

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Liu, Yan. "Text Emotion Classification System Integrating Visual Communication and Deep Learning for Social Platform." Scalable Computing: Practice and Experience 25, no. 3 (2024): 2076–87. http://dx.doi.org/10.12694/scpe.v25i3.2720.

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With the development of modern information technology, social networks have become an im-portant platform for people to express, and at the same time, a large number of texts have been produced. However, the comment text has the characteristics of randomness and colloquialism in the way of expression, and also contains a lot of non-text data. Therefore, manually analyzing the emotional information in the text will consume a lot of time and the accuracy will be limited. To solve emotion classification, this study proposes a knowledge enhanced double loop emotion classification neural network mo

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Caschera, Maria Chiara, Patrizia Grifoni, and Fernando Ferri. "Emotion Classification from Speech and Text in Videos Using a Multimodal Approach." Multimodal Technologies and Interaction 6, no. 4 (2022): 28. http://dx.doi.org/10.3390/mti6040028.

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Emotion classification is a research area in which there has been very intensive literature production concerning natural language processing, multimedia data, semantic knowledge discovery, social network mining, and text and multimedia data mining. This paper addresses the issue of emotion classification and proposes a method for classifying the emotions expressed in multimodal data extracted from videos. The proposed method models multimodal data as a sequence of features extracted from facial expressions, speech, gestures, and text, using a linguistic approach. Each sequence of multimodal d

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Adams, Aubrie, and Weimin Toh. "Student Emotion in Mediated Learning: Comparing a Text, Video, and Video Game." Electronic Journal of e-Learning 19, no. 6 (2021): pp575–587. http://dx.doi.org/10.34190/ejel.19.6.2546.

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Although serious games are generally praised by scholars for their potential to enhance teaching and e-learning practices, more empirical evidence is needed to support these accolades. Existing research in this area tends to show that gamified teaching experiences do contribute to significant effects to improve student cognitive, motivational, and behavioural learning outcomes, but these effects are usually small. In addition, less research examines how different types of mediated learning tools compare to one another in influencing student outcomes associated with learning and motivation. As

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Bharti, Santosh Kumar, S. Varadhaganapathy, Rajeev Kumar Gupta, et al. "Text-Based Emotion Recognition Using Deep Learning Approach." Computational Intelligence and Neuroscience 2022 (August 23, 2022): 1–8. http://dx.doi.org/10.1155/2022/2645381.

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Sentiment analysis is a method to identify people’s attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech and facial expressions for emotion recognition. However, emotion detection in text is a tedious task as cues are missing, unlike in speech, such as tonal stress, facial expression, pitch, etc. To identify e

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Jo, Jaechoon, Soo Kyun Kim, and Yeo-chan Yoon. "Text and Sound-Based Feature Extraction and Speech Emotion Classification for Korean." International Journal on Advanced Science, Engineering and Information Technology 14, no. 3 (2024): 873–79. http://dx.doi.org/10.18517/ijaseit.14.3.18544.

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Embracing the complexities of human emotions conveyed through speech, this study ventures into Speech Emotion Recognition (SER) within the human-computer interaction domain, leveraging cutting-edge artificial intelligence technologies. Focusing on the auditory attributes of speech, such as tone, pitch, and rhythm, the research introduces an innovative approach that amalgamates deep learning techniques with the A Learnable Frontend for Audio Classification (LEAF) algorithm and wav2vec 2.0 pre-trained on a large corpus, specifically targeting Korean voice samples. This methodology underlines the

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Seliverstova, Elena I., Larisa B. Volkova та Xiangfei Ma. "An Emotion-evoking Text in Cross-Сultural Aspect: Attitude and Evalution". RUDN Journal of Language Studies, Semiotics and Semantics 14, № 1 (2023): 260–76. http://dx.doi.org/10.22363/2313-2299-2023-14-1-260-276.

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The success of reading, including reading as a part of foreign language acquisition, depends on numerous factors, such as motivation, academic emotions, engagement in reading, etc. Although the necessity to take into consideration a reader has been pointed out on many occasions, the culture-specific paradigms of emotional situations and their role in learning have hardly been discussed. The current study is aimed at exploring an emotional response to a humorous Russian-language text by representatives of two cultures; its primary target is to identify similarities and differences in the reacti

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Jeong, Eunseo, Gyunyeop Kim, and Sangwoo Kang. "Multimodal Prompt Learning in Emotion Recognition Using Context and Audio Information." Mathematics 11, no. 13 (2023): 2908. http://dx.doi.org/10.3390/math11132908.

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Prompt learning has improved the performance of language models by reducing the gap in language model training methods of pre-training and downstream tasks. However, extending prompt learning in language models pre-trained with unimodal data to multimodal sources is difficult as it requires additional deep-learning layers that cannot be attached. In the natural-language emotion-recognition task, improved emotional classification can be expected when using audio and text to train a model rather than only natural-language text. Audio information, such as voice pitch, tone, and intonation, can gi

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Quan, Changqin, and Fuji Ren. "Visualizing Emotions from Chinese Blogs by Textual Emotion Analysis and Recognition Techniques." International Journal of Information Technology & Decision Making 15, no. 01 (2016): 215–34. http://dx.doi.org/10.1142/s0219622014500710.

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The research on blog emotion analysis and recognition has become increasingly important in recent years. In this study, based on the Chinese blog emotion corpus (Ren-CECps), we analyze and compare blog emotion visualization from different text levels: word, sentence, and paragraph. Then, a blog emotion visualization system is designed for practical applications. Machine learning methods are applied for the implementation of blog emotion recognition at different textual levels. Based on the emotion recognition engine, the blog emotion visualization interface is designed to provide a more intuit

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Solanki, Ms Pinal. "A Study on Emotion Detection & Classification from Text using Machine Learning." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 22 (March 26, 2022): 40–46. http://dx.doi.org/10.55529/jaimlnn.22.40.46.

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Humans are using online social networks to share their opinions and thoughts on a variety of subjects and topics with their friends, family, and relations through text, photographs, audio and video messages and posts. On specific social, national, and global topics, humans can share their thoughts, mental states, moments, and viewpoints. Given the variety of communication options available, text is one of the most popular mediums of communication on social media. The study described here aims to detect and analyses sentiment and emotion expressed by people in their messages, and then use that

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VAILTOOT ABDUR RAZZAQ and MANEESHA PATAN. "Emotion Detection using Twitter Datasets and Spacy Algorithm." international journal of engineering technology and management sciences 8, no. 3 (2024): 46–58. http://dx.doi.org/10.46647/ijetms.2024.v08i03.007.

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People show emotions for everyday communication. Emotions are identified by facial expressions, behavior, writing, speaking, gestures and physical actions. Emotion plays a vital role in the interaction between two people. The detection of emotions through text is a challenge for researchers. Emotion detection from the text can be useful for real-world application. Automatic emotion detection in the original text aims to recognize emotions in any digital medium by using natural language processing techniques and different approaches. Enabling machines with the ability to recognize emotions in a

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