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1
Srinidhi, K., T. L.S Tejaswi, CH Rama Rupesh Kumar, and I. Sai Siva Charan. "An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis." International Journal of Engineering & Technology 7, no. 2.32 (2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.
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We propose an advanced well-trained sentiment analysis based adoptive analysis “word specific embedding’s, dubbed sentiment embedding’s”. Using available word and phrase embedded learning and trained algorithms mainly make use of contexts of terms but ignore the sentiment of texts and analyzing the process of word and text classifications. sentimental analysis on unlike words conveying same meaning matched to corresponding word vector. This problem is bridged by combining encoding opinion carrying text with sentiment embeddings words. But performing sentimental analysis on e-commerce, social networking sites we developed neural network based algorithms along with tailoring and loss function which carry feelings. This research apply embedding’s to word-level, sentence-level sentimental analysis and classification, constructing sentiment oriented lexicons. Experimental analysis and results addresses that sentiment embedding techniques outperform the context-based embedding’s on many distributed data sets. This work provides familiarity about neural networks techniques for learning word embedding’s in other NLP tasks.
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Anannya, Gupta, Deepali, Ahlawat Manvesh, Vedant, and Sharma Swati. "TWEEZER – Tweets Analysis." International Journal of Engineering and Management Research 10, no. 2 (2020): 111–15. https://doi.org/10.31033/ijemr.10.2.12.
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<strong>Twitter is one in all the foremost used applications by the people to precise their opinion and show their sentiments towards different occasions. Sentiment analysis is an approach to retrieve the sentiment through the tweets of the general public. Twitter sentiment analysis is application for sentiment analysis of information which are extracted from the twitter(tweets). With the assistance of twitter people get opinion about several things round the nation .Twitter is one such online social networking website where people post their views regarding to trending topics .It s huge platform having over 317 million users registered from everywhere the globe. a decent sentimental analysis of information of this huge platform can result in achieve many new applications like – Movie reviews, Product reviews, Spam detection, Knowing consumer needs, etc. during this paper, we used two specific algorithm –Naïve Bayes Classifier Algorithm for polarity Classification & Hashtag classification for top modeling. this system individually has some limitations for Sentiment analysis. The goal of this report is to relinquish an introduction to the present fascinating problem and to present a framework which is able to perform sentiment analysis on online mobile reviews by associating modified naïve bayes means algorithm with Naïve bayes classification.</strong>
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Sunil Kumar, V., Vedashree C.R, and Sowmyashree S. "IMAGE SENTIMENTAL ANALYSIS: AN OVERVIEW." International Journal of Advanced Research 10, no. 03 (2022): 361–70. http://dx.doi.org/10.21474/ijar01/14398.
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Visual content, such as photographs and video, contains not only objects, locations, and events, but also emotional and sentimental clues. On social networking sites, images are the simplest way for people to communicate their emotions. Images and videos are increasingly being used by social media users to express their ideas and share their experiences. Sentiment analysis of such large-scale visual content can aid in better extracting user sentiments toward events or themes, such as those in image tweets, so that sentiment prediction from visual content can be used in conjunction with sentiment analysis of written content. Despite the fact that this topic is relatively new, a wide range of strategies for various data sources and challenges have been created, resulting in a substantial body of study. This paper introduces the area of Image Sentiment Analysis and examines the issues that it raises. A description of new obstacles is also included, as well as an assessment of progress toward more sophisticated systems and related practical applications, as well as a summary of the studys findings.
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Tang, Duyu, Furu Wei, Bing Qin, Nan Yang, Ting Liu, and Ming Zhou. "Sentiment Embeddings with Applications to Sentiment Analysis." IEEE Transactions on Knowledge and Data Engineering 28, no. 2 (2016): 496–509. http://dx.doi.org/10.1109/tkde.2015.2489653.
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Mishra, Abhinav, Amit Ranjan, Arin Kumar, Vikash Sharma, and Projjwal Biswas. "Sentiment Analysis Using NLP." International Journal of Research 10, no. 11 (2023): 105–12. https://doi.org/10.5281/zenodo.10211177.
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<strong>Sentiment analysis, often referred to as opinion mining, is a vital subfield of Natural Language Processing (NLP) thatfocuses on understanding and classifying sentiments expressed in text data. This paper offers a comprehensive exploration of sentiment analysis, encompassing its methodologies, applications across various domains, and practical implications. We delve into the specifics of data collection, preprocessing, feature extraction, sentiment analysis techniques, and present empirical findings that highlight the effectiveness of our approach.</strong>
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H M, Dr Keerthi. "Review on Sentimental Analysis and Aspect Analysis with Codemix using LLM, BERT, and Naive Bayes." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40598.
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In the era of social media, where multilingual conversations are prevalent, analyzing code-mixed text poses unique challenges. This project presents a comparative analysis of sentiment analysis and aspect-based sentiment analysis on code-mixed data using advanced techniques like Large Language Models (LLM), BERT, and Naive Bayes. Sentiment analysis categorizes text into positive, negative, or neutral sentiments, while aspect-based analysis identifies opinions on specific topics, such as "price" or "quality" in reviews. By focusing on code-mixed text, this study compares the effectiveness of each method in understanding sentiments and specific opinions, paving the way for improved applications in multilingual settings.
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Mutmainah, Siti, Dhomas Hatta Fudholi, and Syarif Hidayat. "Analisis Sentimen dan Pemodelan Topik Aplikasi Telemedicine Pada Google Play Menggunakan BiLSTM dan LDA." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 312. http://dx.doi.org/10.30865/mib.v7i1.5486.
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The pandemic caused by the 2019 coronavirus has revitalized telemedicine as information and communication technology-based health services and as a medium for doctors' services in diagnosing, treating, preventing and evaluating health conditions. One of the telemedicine service applications in Indonesia is Alodokter, Halodoc, KlikDokter, SehatQ and YesDok. Previous research on the same domain, namely applications telemedicine uses machine learning to perform sentiment modeling. This research performs sentiment analysis using the BiLSTM method (Bidirectional Long Short-Term Memory) which can better represent contextual information and can read user feedback information in both directions. Then sentiment analysis is described explicitly to identify topics from user sentiment using LDA (Latent Dirichlet Allocation). User feedback was collected on August 14, 2022 which was obtained in the five applications totaling 244,098. The results of the analysis on feedback obtained were 112,013 positive sentiments, 34,853 neutral sentiments and 97,228 negative sentiments. The BiLSTM and Word2Vec models used have a good performance in classifying sentiments, namely 95%, while the topic modeling for each sentiment has a coherence value of 0.6437 on positive topics, 0.6296 neutral sentiments and 0.6132 negative sentiments.
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Prof., Richa Mehra, Saxena Diksha, and Gupta |. Joy Joseph Shubham. "Sentiment Analysis." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1370–73. https://doi.org/10.31142/ijtsrd23375.
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Sentiment Analysis SA is an ongoing field of research in text mining field. SA sentiment analysis is the computational treatment of opinions, sentiments and text. This s paper deals in a comprehensive overview of the recent updates in this field. Many recently proposed algorithms amend and various SA applications are investigated and presented briefly in this paper. The related fields to SA transfer learning, emotion detection, and building resources that attracted researchers recently are discussed. The main objective of this paper is to give nearly full image of SA techniques and the related fields with brief details. The main contributions in this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas. Prof. Richa Mehra | Diksha Saxena | Shubham Gupta | Joy Joseph "Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23375.pdf
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Zougris, Konstantinos. "Origins, Styles, and Applications of Text Analytics in Social Science Research." Encyclopedia 5, no. 2 (2025): 70. https://doi.org/10.3390/encyclopedia5020070.
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Textual analysis is grounded in conceptual schemes of traditional qualitative and quantitative content analysis techniques that have led to the hybridization of methodological styles widely used across social scientific fields. This paper delivers an extensive review of the origins and evolution of text analysis within the domains of traditional content analysis. Emphasis is given to the conceptual schemas and operational structure of latent semantic analysis, and its capacity to detect topical clusters of large corpora. Further, I describe the operations of Entity–Aspect Sentiment Analysis which are designed to measure and assess sentiments/opinions within specific contextual domains of textual data. Then, I conceptualize and elaborate on the potential of streamlining latent semantic and Entity–Aspect Sentiment Analysis complemented by Correspondence Analysis, generating an integrated operational scheme that would detect the topic structure, assess the contextual sentiment/opinion for each detected topic, test for statistical dependence of sentiments/opinions across topical domains, and graphically display conceptual maps of sentiments in topics space.
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SK, Syed Zabiulla, and Mausumi Goswami. "Sentiment Analysis Approaches and Applications –A Review." December 2023 5, no. 4 (2023): 381–98. http://dx.doi.org/10.36548/jucct.2023.4.004.
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With the advent of smartphones and the ease of access to the internet, people are mainly interested in sending textual messages through social media platforms. In many cases, customers would like to review the services provided by different providers in order to express satisfaction or dissatisfaction. The sentiments of users make a huge difference in the success of any business idea in the present digital age. As there are many competitors in every field of technology, health, and education, people would selectively want to use the resources that have positive opinions about them from the user community in the online reviews. There are different techniques to effectively estimate the user reviews, whether they are for or against a particular concept or the product. There are different techniques, like lexicon-based techniques, machine learning-based techniques, and deep learning-based techniques which are used to analyse the sentiments of the users’ reviews in order to improve user expectations. Lexicon-based techniques have many challenges, like the wrong interpretation of the meanings of the words and giving wrong sentiment scores to the words used by ignoring the grammatical constraints in the user reviews. There are many machine learning algorithms, like Logistic regression (LR), and Support Vector Machines (SVM) which can overcome the shortcomings of lexicon-based sentiment analysis models and could be used in various spheres of applications. The manuscript presents a detailed study in this regard.
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Keerthi H M, Dr. "Aspect Based Sentiment Analysis on Codemix Languages." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48037.
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Abstract In the era of social media, where multilingual conversations are prevalent, analyzing code-mixed text poses unique challenges. This project presents a comparative analysis of sentiment analysis and aspect-based sentiment analysis on code-mixed data using advanced techniques like Large Language Models (LLM), BERT, and Naive Bayes. Sentiment analysis categorizes text into positive, negative, or neutral sentiments, while aspect-based analysis identifies opinions on specific topics, such as "price" or "quality" in reviews. By focusing on code-mixed text, this study compares the effectiveness of each method in understanding sentiments and specific opinions, paving the way for improved applications in multilingual settings.
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SrinivasRao, Lakshmi, Sravan Kumar, Nagender Reddy, Jayavardhan Reddy, and Buvann Buvann. "A Study of Sentiment Analysis on Mobile Applications." International Journal of Research Publication and Reviews 5, no. 5 (2024): 8035–38. http://dx.doi.org/10.55248/gengpi.5.0524.1325.
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Kumar, Ravindra. "Methods to Perform Opinion Mining and Sentiment Analysis to Detect Factors Affecting Mental Health." International Journal of Engineering and Advanced Technology 11, no. 1 (2021): 70–72. http://dx.doi.org/10.35940/ijeat.f3025.1011121.
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Sentimental analysis and opinion extraction are emerging fields at AI. These approaches help organizations to use the opinions, sentiments, and subjectivity of their consumers in decision-making. Sentiments, views, and opinions show the feeling of the consumers towards a given product or service. In recent years, Opinion Mining and Sentiment Analysis has become an important tool to detect the factors affecting mental health. It’s Also true that human biasness is available in giving opinions, but it can be eliminated through the use of algorithms to get better results. However, it is crucial to remember that the developers are human and might pass the biasness to the algorithms during training. The main target of this paper is to give background knowledge on opinion extraction and sentimental analysis and how factors affecting mental health can be collected. The paper aimed to use interested individuals in knowing some of the algorithms in opinions extraction and sentimental analysis. The paper also provides benefits of using sentiment analysis and some of the challenges of using the algorithms.
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Merlin Durairaj John Louis, Anitha, and Vimal Kumar Dhanasekaran. "SentimentLP: unveiling advanced sentiment analysis through Leptotila optimization-based gradient boosting machines." Bulletin of Electrical Engineering and Informatics 14, no. 2 (2025): 1212–22. https://doi.org/10.11591/eei.v14i2.8959.
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Sentiment analysis is pivotal in extracting insights from textual data, enabling organizations to understand customer opinions, market trends, and brand perception. This study introduces a novel approach, SentimentLP, which integrates Leptotila optimization (LPO) with gradient boosting machines (GBM) for sentiment analysis tasks. The proposed framework leverages LPO’s dynamic optimization capabilities to enhance GBM models’ performance in sentiment classification. Through iterative refinement and adaptive learning, SentimentLP optimizes feature extraction, model training, and ensemble learning processes, improving sentiment analysis accuracy and efficiency. Results from various evaluation metrics, including precision, recall, classification accuracy, and F-measure, demonstrate the effectiveness of SentimentLP in accurately capturing sentiment expressions in text data. Additionally, the fusion of LPO with GBM ensures scalability, adaptability, and interpretability of sentiment analysis models, making SentimentLP a valuable tool for extracting actionable insights from textual data across diverse domains and applications.
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Amina, Samih, Ghadi Abderrahim, and Fennan Abdelhadi. "Enhanced sentiment analysis based on improved word embeddings and XGboost." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1827–36. https://doi.org/10.11591/ijece.v13i2.pp1827-1836.
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Sentiment analysis is a well-known and rapidly expanding study topic in natural language processing (NLP) and text classification. This approach has evolved into a critical component of many applications, including politics, business, advertising, and marketing. Most current research focuses on obtaining sentiment features through lexical and syntactic analysis. Word embeddings explicitly express these characteristics. This article proposes a novel method, improved words vector for sentiments analysis (IWVS), using XGboost to improve the F1-score of sentiment classification. The proposed method constructed sentiment vectors by averaging the word embeddings (Sentiment2Vec). We also investigated the Polarized lexicon for classifying positive and negative sentiments. The sentiment vectors formed a feature space to which the examined sentiment text was mapped to. Those features were input into the chosen classifier (XGboost). We compared the F1-score of sentiment classification using our method via different machine learning models and sentiment datasets. We compare the quality of our proposition to that of baseline models, term frequency-inverse document frequency (TF-IDF) and Doc2vec, and the results show that IWVS performs better on the F1-measure for sentiment classification. At the same time, XGBoost with IWVS features was the best model in our evaluation.
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Das, Mala. "Exploring Sentiment Analysis across Text, Audio, and Video: A Comprehensive Approach and Future Directions." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 3129–34. http://dx.doi.org/10.22214/ijraset.2024.62244.
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Abstract: This study presents a comprehensive exploration of sentiment analysis techniques across text, audio, and video modalities. Leveraging natural language processing (NLP), speech recognition, and computer vision algorithms, the research demonstrates the versatility and adaptability of sentiment analysis across diverse data sources. The necessity of such an approach lies in its ability to provide deeper insights into user emotions and opinions expressed in various mediums, including written text, spoken language, and visual content. Moreover, the study highlights the importance of sentiment analysis in understanding customer feedback, market trends, social media sentiments, and sentiment-aware recommendation systems. Future directions include advancing algorithmic accuracy and efficiency, integrating multimodal fusion techniques, and exploring applications in diverse domains, thereby paving the way for enhanced sentiment analysis capabilities and broader realworld applications
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Setiawan, Boy. "A Review of Sentiment Analysis Applications in Indonesia Between 2023-2024." Journal of Information Engineering and Educational Technology 8, no. 2 (2025): 71–83. https://doi.org/10.26740/jieet.v8n2.p71-83.
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The landscape of sentiment analysis applications in Indonesia is on the rise with the many published papers on the subject over the years. The need to predict sentiment coincides with the rise of social media and how the public uses it to express sentiments toward an interesting topic. The lack of tools for working with the Indonesian language has brought the invention of libraries to tackle the difficulty and uniqueness of the language on various topics from diverse data sources. The introduction of Sarkawi as a stemmer helps researchers overcome dimensionality problems commonly found with text processing, and boosts the performance of machine learning (ML) models. Using InSet as a lexicon dictionary capable of performing sentiment prediction has started gaining popularity for automatic labeling. The development of IndoBERT, an advanced neural network (NN) large language model (LLM) specifically trained from a large Indonesian text corpus capable of more than sentiment analysis, has gained traction both for automatic labeling and prediction models. Although the majority of research revolves around Naïve Bayes (NB), State Vector Machine (SVM), and K-Nearest Neighbor (KNN) the future of sentiment analysis applications in Indonesia could be heading towards a more advanced deep learning architecture. Finally, this study is intended as a basis for future research in the applications of sentiment analysis in Indonesia and the development of the language.
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Yadav, Pinky, Indu Kashyap, and Bhoopesh Bhati. "Contextual Ambiguity Framework for Enhanced Sentiment Analysis." Tehnički glasnik 18, no. 3 (2024): 385–93. http://dx.doi.org/10.31803/tg-20231227064230.
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Negation is a universal linguistic phenomenon that affects the performance of Natural Language Processing (NLP) applications, especially opinion mining data. Many words exists in sentences that have multiple interpretations or sentiments depending on how they are placed with respect to the negation word in the sentence. A cutting-edge framework is designed to tackle the nuanced challenge of detecting contextual ambiguity through negation in sentiment analysis. The approach uniquely combines advanced natural language processing techniques with deep linguistic insights, enabling the accurate interpretation of sentiment in complex sentences where negation plays a key role. The framework identifies negation cues and their scope, then assesses their impact on sentiment, considering contextual dependencies and word semantics. The model's innovation lies in context-sensitive algorithms that adeptly handle different sentence structures and idiomatic expressions, a notable advancement over traditional sentiment analysis tools. Particularly effective in interpreting sarcastic or ironic statements, the framework significantly outperforms existing models in accuracy, especially in negation-heavy contexts. This advancement enhances sentiment analysis applications like social media monitoring and customer feedback analysis, offering a more nuanced understanding of public opinion.
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Alromema, Waseem. "Sentiment Analysis Applications during COVID-19 Pandemics: An Exploratory Review." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 6 (2022): 114–18. http://dx.doi.org/10.35940/ijrte.f6855.0310622.
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Coronavirus pandemic has created complex challenges and adverse conditions. Sentiment analysis is a process of studying the user application. Because of using the internet in daily activities, many domains and organizations concentrate on analysis or getting user feedback to take the right decision. This paper is review the existing applications that used a sentiments analysis to identify major sentiment trends associated with the push to reopen the analyzing sentiment in social media like Twitter, etc. Data time aligned to the COVID-19 reopening debate. In addition, discover the most popular techniques and approaches. This study focus the research articles in high impact journals that published during the epidemics from 2019 to 2021. The research question that this study answer it are. This study can be beneficial to many domains such as sentiment analysis, text mining, research in related areas, and postgraduate students. This research could present valuable time sensitive opportunities for governments, and the nation into a successful new normal future. Several applications have employed in several domains, including tourism, education, business and health. Health information can be disseminated by social media and misinformation can be addressed via this platform.
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Waseem, Alromema. "Sentiment Analysis Applications during COVID-19 Pandemics: An Exploratory Review." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 6 (2022): 114–18. https://doi.org/10.35940/ijrte.F6855.0310622.
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<strong>Abstract:</strong> Coronavirus pandemic has created complex challenges and adverse conditions. Sentiment analysis is a process of studying the user application. Because of using the internet in daily activities, many domains and organizations concentrate on analysis or getting user feedback to take the right decision. This paper is review the existing applications that used a sentiments analysis to identify major sentiment trends associated with the push to reopen the analyzing sentiment in social media like Twitter, etc. Data time aligned to the COVID19 reopening debate. In addition, discover the most popular techniques and approaches. This study focus the research articles in high impact journals that published during the epidemics from 2019 to 2021. The research question that this study answer it are. This study can be beneficial to many domains such as sentiment analysis, text mining, research in related areas, and postgraduate students. This research could present valuable time sensitive opportunities for governments, and the nation into a successful new normal future. Several applications have employed in several domains, including tourism, education, business and health. Health information can be disseminated by social media and misinformation can be addressed via this platform.
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Arif Lubis, Fauzi. "User Sentiment Analysis Towards Islamic Banking Applications in Indonesia." Journal of Islamic Economic and Business Research 4, no. 1 (2024): 126–43. http://dx.doi.org/10.18196/jiebr.v4i1.342.
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This study aims to analyze user sentiment towards Islamic banking applications in Indonesia, focusing on Islamic banking applications that have the lowest ratings on Google Playstore. The analysis was conducted to understand perceptions, satisfaction, and factors that influence user experience towards both applications in the context of Islamic economics. The research method used is a quantitative approach with sentiment analysis techniques on user reviews taken from Google Playstore. Data was collected by scraping user reviews and analyzed using the Natural Language Processing (NLP) method to identify positive and negative sentiments. In addition, descriptive analysis was used to explore the main themes that emerged in the user reviews. The results showed that the trust factor was the main contributor to positive sentiment, reaching 45.16% of the total positive sentiment. The feature factor was also significant, contributing 27.90%, while the Sharia aspect factor contributed 6.29%. The technical and service factors contributed 11.61% and 5.97%, respectively. These findings indicate that trust in the application, including security and integrity, as well as relevant features, is an essential element in shaping positive user sentiment. This study provides valuable insights for Islamic banking application developers and related parties to understand user needs and expectations. In addition, these findings highlight the importance of improving aspects of trust and application features to increase user satisfaction and loyalty. These findings contribute to the literature on Islamic economics and Islamic banking app development in the digital era and provide practical guidance to improve application performance in the Indonesian digital banking market.
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Meilan Yang. "English Sentiment Analysis and its Application in Translation Based on Decision Tree Algorithm." International Journal of Maritime Engineering 1, no. 1 (2024): 395–408. http://dx.doi.org/10.5750/ijme.v1i1.1371.
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Sentimental analysis belongs to the class of Natural Language Processing (NLP) based on the rule and machine model. The proposed model comprises of the pre-defined function for the estimation of the features in the English statements. This paper presents the Reflect Sentiment Translation Decision Tree (RSTDT), a novel model designed to integrate sentiment analysis and translation tasks for English text. The RSTDT model combines the strengths of decision tree algorithms with feature extraction techniques to accurately analyze sentiment and translate text across languages. The proposed RSTDT dataset comprises English sentences with annotated sentiment labels, the RSTDT model is trained to identify sentiment polarity and generate corresponding translations in Arabic. The proposed RSTDT model uses Traslation mapping for the estimation of the sentimental features. In order to estimate and classify the features in the neural network, the processes features are assessed using the decision tree model. The RSTDT model's efficacy in precisely capturing sentiment nuances and generating linguistically appropriate translations was shown through thorough testing and review. The model achieves high accuracy in sentiment analysis and exhibits proficiency in translating sentiment-rich content into Arabic while maintaining contextual relevance. Additionally, robust classification performance metrics underscore the model's efficacy in accurately classifying English words into sentiment categories. The RSTDT model offers a promising solution for multilingual sentiment analysis applications, with potential applications in social media monitoring, customer feedback analysis, and cross-cultural sentiment analysis.
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Gupta, Aayush, Anant Gandhi, Saarthak Agarwal, Shamin Chokshi, and Saravanakumar K. "Sentiment Analysis-Enhancements and Applications." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (2021): 56–70. http://dx.doi.org/10.35940/ijrte.a1409.1110421.
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The concept of Natural Language Processing that deals with problems of identifying the sentiment from the voice or text of a speaker or writer and then use that analysis further for making predictions, market survey, customer service, product satisfaction, precision targeting etc. is called Sentiment analysis. From one viewpoint, it is an abstract evaluation of something dependent on close to home observational experience. It Is mostly established in target realities and incompletely governed by feelings. Then again, a sentiment can be deciphered as a kind of measurement in the information in regards to a specific subject. It is a lot of markers that mix present a point of view, i.e., perspective for the specific issue. So as to enhance the accuracy of sentiment analysis/classification, it is imperative to appropriately recognize the semantic connections between the various words and phrases that are describing the subject or aspect. This can be done by applying semantic analysis with a syntactic parser and supposition vocabulary. This research will discuss different sets of approaches for application or domain specific problems and then compare them to obtain the best possible approaches to the problem of sentiment analysis.
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Aayush, Gupta, Gandhi Anant, Agarwal Saarthak, Chokshi Shamin, and Kandasamy Saravanakumar. "Sentiment Analysis-Enhancements and Applications." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (2021): 56–70. https://doi.org/10.35940/ijrte.A1409.1110421.
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The concept of Natural Language Processing that deals with problems of identifying the sentiment from the voiceor text of a speaker or writer and then use that analysis furtherfor making predictions, market survey, customer service, product satisfaction, precision targeting etc. is called Sentiment analysis. From one viewpoint, it is an abstract evaluation of something dependent on close to home observational experience. It Is mostly established in target realities and incompletely governed by feelings. Then again, a sentiment can be deciphered as a kind of measurement in the information in regards to a specific subject. It is a lot of markers that mix present a point of view, i.e., perspective for the specific issue. So as to enhance the accuracy of sentiment analysis/classification, it is imperative to appropriately recognize the semantic connections between the various words and phrases that are describing the subject or aspect. This can be done by applying semantic analysis with a syntactic parser and supposition vocabulary. This research will discuss different sets of approaches for application or domain specific problems and then compare them to obtain the best possible approaches to the problem of sentiment analysis.
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Samih, Amina, Abderrahim Ghadi, and Abdelhadi Fennan. "Enhanced sentiment analysis based on improved word embeddings and XGboost." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1827. http://dx.doi.org/10.11591/ijece.v13i2.pp1827-1836.
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<span lang="EN-US">Sentiment analysis is a well-known and rapidly expanding study topic in natural language processing (NLP) and text classification. This approach has evolved into a critical component of many applications, including politics, business, advertising, and marketing. Most current research focuses on obtaining sentiment features through lexical and syntactic analysis. Word embeddings explicitly express these characteristics. This article proposes a novel method, improved words vector for sentiments analysis (IWVS), using XGboost to improve the F1-score of sentiment classification. The proposed method constructed sentiment vectors by averaging the word embeddings (Sentiment2Vec). We also investigated the Polarized lexicon for classifying positive and negative sentiments. The sentiment vectors formed a feature space to which the examined sentiment text was mapped to. Those features were input into the chosen classifier (XGboost). We compared the F1-score of sentiment classification using our method via different machine learning models and sentiment datasets. We compare the quality of our proposition to that of baseline models, term frequency-inverse document frequency (TF-IDF) and Doc2vec, and the results show that IWVS performs better on the F1-measure for sentiment classification. At the same time, XGBoost with IWVS features was the best model in our evaluation.</span>
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Verma, Divya. "Sentiment Analysis with ChatGPT Across Domains." Don Bosco Institute of Technology Delhi Journal of Research 1, no. 1 (2024): 19–26. https://doi.org/10.48165/dbitdjr.2024.1.01.04.
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This paper explores ChatGPT’s applications in sentiment analysis across various domains, including business, Reddit data, and scientific citations. It demonstrates ChatGPT’s utility in understanding customer needs and public opinion, as well as its ability to identify nuanced sentiment and biases in scholarly research evaluation. Analysis of app reviews on the Google Play Store shows predominantly positive sentiments, with models like Random Forest and SVM achieving high effectiveness. The research evaluates ChatGPT and other large language models such as Gemini and LLaMA for multilingual sentiment analysis, revealing their proficiency alongside biases and inconsistencies across languages. The study emphasizes the importance of standardized evaluation methodologies and the need for data and algorithm improvements to enhance ChatGPT’s performance and applicability in sentiment analysis.
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Gayki, Miss Yashashri, Mr Pratham Chatke, Miss Rani Nandane, et al. "A Survey on “Sentiment Analysis of E-commerce Website’s Reviews”." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1627–31. http://dx.doi.org/10.22214/ijraset.2023.56255.
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Abstract: Sentiment analysis, a vital component of natural language processing, has gained significant relevance in the realm of ecommerce websites. In this digital age, where consumers heavily rely on online reviews to inform their purchase decisions, understanding and harnessing sentiment in ecommerce reviews is paramount. This abstract explores the utilization of sentiment analysis techniques to extract valuable insights from customer feedback, offering a panoramic view of its applications, challenges, and implications. We delve into keyword extraction, sentiment polarity classification, and the integration of sentiment analysis into recommendation systems. This paper also examines the evolving role of sentiment analysis in enhancing user experiences, brand reputation management, and product development. By decoding the sentiments hidden within ecommerce website reviews, businesses can strategically adapt, improve customer satisfaction, and thrive in a highly competitive online marketplace
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Gnanapriya, Dr S. Gnanapriya, and R. Arun Kumar. "Fraud App Detection Using Sentiment Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42480.
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Since there are more and more mobile applications used in daily life, it's critical to monitor which ones are secure and which are not. Based just on the reviews listed for each program, one cannot determine how reliable and safe each one is. Therefore, it is essential to verify and start a system to ensure that the apps are authentic or fraudulent. The goal is to create a web system that uses support vector machines and sentiment analysis to identify fraudulent apps before users download them. The purpose of sentiment analysis is to assist in identifying the emotional undertones of words used in online communication. This approach is helpful for keeping an eye on social media and for quickly gauging public sentiment on particular topics. On the internet, the customer may not always find accurate or genuine product reviews. The reviews could be authentic or fraudulent. We can ascertain whether or not the app is authentic by examining evaluations that include remarks from both users and administrators. The system can learn and understand the sentiments and emotions of reviews and other materials by using support vector machines and sentimental analysis. One of the main components of app ranking fraud is the manipulation of reviews. The right app for iOS and Android can be found by examining reviews and comments using emotional analysis and support vector machines. Keywords : Positive negative neutral reviews, Sentiment analysis, Support Vector Machine, Users reviews
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Thelwall, Mike. "Gender bias in sentiment analysis." Online Information Review 42, no. 1 (2018): 45–57. http://dx.doi.org/10.1108/oir-05-2017-0139.
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Purpose The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females. Design/methodology/approach This paper uses data sets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females. Findings Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis. Research limitations/implications Only one lexical sentiment analysis algorithm was used. Practical implications Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results. Originality/value This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.
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Ramesh, Chundi, R. Hulipalled Vishwanath, and Bharthish Simha Jay. "Lexicon-based sentiment analysis for Kannada-English code-switch text." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1500–1507. https://doi.org/10.11591/ijai.v12.i3.pp1500-1507.
Full textAbstract:
Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic and product. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning, machine learning, or a Lexicon-based approach. Machine learning and deep learning methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with machine learning and deep learning. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better Accuracy of 83.2% and 83% of F1-score.
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Muh. Subhan. "Exploring Public Sentiment Toward Artificial Intelligence Apps: A Case Study of ChatGPT, Gemini, and DeepSeek in Google Apps." Journal of Information Systems Engineering and Management 10, no. 19s (2025): 203–11. https://doi.org/10.52783/jisem.v10i19s.3007.
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Introduction: Artificial intelligence (AI) has witnessed rapid advancements in recent decades, impacting various sectors such as business, education, and entertainment. AI-based applications have become integral to daily interactions, with platforms like Google hosting popular applications such as ChatGPT, Gemini, and DeepSeek. These AI applications offer distinct approaches to technology but have the potential to influence public sentiment toward AI broadly. However, public perception remains diverse, with some embracing AI for its potential, while others express concerns regarding its implications, such as job displacement and privacy issues. Objectives: This study aims to explore the factors that shape public sentiment toward three AI applications—ChatGPT, Gemini, and DeepSeek. Specifically, it addresses the following research questions: (1) What factors influence public sentiment toward these AI applications? (2) How do the sentiments differ between these applications? (3) To what extent is public sentiment reflective of broader perceptions of AI technology? Methods: The research employs a case study approach, collecting user reviews from Google Play Store for ChatGPT, Gemini, and DeepSeek. Data preprocessing includes removing null entries, normalizing text, and performing tokenization. Sentiment classification is conducted using the Ekman’s Six Basic Emotions model, and sentiment analysis is enhanced using machine learning models, specifically Naive Bayes (NB) and Logistic Regression (LR). The models’ performance is evaluated based on AUC, Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). Results: The analysis reveals that User Interaction and App Performance are the primary factors influencing public sentiment. ChatGPT receives the highest level of positive sentiment, particularly for its interactive capabilities. While Gemini also receives favorable reviews, its focus on intelligent search results in slightly less positive sentiment compared to ChatGPT. DeepSeek displays a more mixed sentiment, with some users appreciating its depth in data analysis, but many expressing dissatisfaction with its user interaction. Sentiment analysis further demonstrates that Joy and Surprise were the dominant emotions for ChatGPT, whereas Fear and Disgust were less prevalent across all applications. Conclusions: This study concludes that user interaction and performance significantly drive public sentiment toward AI applications. While concerns over security and privacy exist, they are less influential compared to the experience users have with the application's functionality. The findings highlight the importance of enhancing user experience and performance for AI adoption. Additionally, the research provides insights into the need for further transparency regarding data privacy and the ethical use of AI.
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Hu, Zhengbing, Ivan Dychka, Kateryna Potapova, and Vasyl Meliukh. "Augmenting Sentiment Analysis Prediction in Binary Text Classification through Advanced Natural Language Processing Models and Classifiers." International Journal of Information Technology and Computer Science 16, no. 2 (2024): 16–31. http://dx.doi.org/10.5815/ijitcs.2024.02.02.
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Sentiment analysis is a critical component in natural language processing applications, particularly for text classification. By employing state-of-the-art techniques such as ensemble methods, transfer learning and deep learning architectures, our methodology significantly enhances the robustness and precision of sentiment predictions. We systematically investigate the impact of various NLP models, including recurrent neural networks and transformer-based architectures, on sentiment classification tasks. Furthermore, we introduce a novel ensemble method that combines the strengths of multiple classifiers to improve the predictive ability of the system. The results demonstrate the potential of integrating state-of-the-art Natural Language Processing (NLP) models with ensemble classifiers to advance sentiment analysis. This lays the foundation for a more advanced comprehension of textual sentiments in diverse applications.
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Ghatora, Pawanjit Singh, Seyed Ebrahim Hosseini, Shahbaz Pervez, Muhammad Javed Iqbal, and Nabil Shaukat. "Sentiment Analysis of Product Reviews Using Machine Learning and Pre-Trained LLM." Big Data and Cognitive Computing 8, no. 12 (2024): 199. https://doi.org/10.3390/bdcc8120199.
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Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data.
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Sivakumar, R. D. Assistant Professor Department of Computer Science, and S. Former Assistant Professor of Business Administration Brindha. "SENTIMENT ANALYSIS FOR CONSUMER BEHAVIOR PREDICTION." Indian Journal of Research and Development Systems in Technologization 1, no. 2 (2024): 40–48. https://doi.org/10.5281/zenodo.10953122.
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<em>Sentiment analysis has proven to be an indispensable tool in consumer behavior analysis, and it is utilized for predicting people's actions and choice based on the given sentiments. This article investigates a blend of sentiment analysis technique with conventional consumer behavior analytic approaches that expands prognostic ability. Commencing with a general introduction which covers the basic concepts of sentiment analysis such as what it is, how it is done, and where it is applied, the paper brings to the limelight how the study of what customers feel is crucial during this digital era. Sentiment Analysis is a type of text analytics which is being employed to make sense of data from website, social media, product reviews, feedback surveys, and many more companies have started to use the tool to improve on their consumer targeting strategies. The paper deliberates on a wide variety of sentiment analysis approaches, ranging from lexicon based techniques to advanced machine learning and deep learning models, whereby these applications are highlighted as being useful in the extendibility of sentiments and emotions. Sentiment analysis is also examined, with a focus on analyzing trends across different industries including retail, hospitality, finance, and healthcare. The paper looks into the effect of sentiment analysis on business performance over a period of time and how this leads to market outcomes. As well, the paper tackles ethical concerns with sentiment mining, including privacy worries and discrimination, stressing out transparency and responsible data usage through consumer data. Consumers’ sentiment and behavior are getting filtered with sentiment analysis and it provides the businesses the measurable data and actionable insights which in turn improves the decision-making abilities, personalized marketing approach, and customer satisfaction and loyalty. This article aims at covering the area of consumer behavior analysis which emphasizes the place of emotional analysis in consumer sentiment deciphering and ensuing individual choice prediction.</em>
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Su, Zihan. "Applications of BERT in sentimental analysis." Applied and Computational Engineering 92, no. 1 (2024): 147–52. http://dx.doi.org/10.54254/2755-2721/92/20241711.
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This research study emphasizes sentiment analysis and examines Natural Language Processing (NLP) by Bidirectional Encoder Representations from Transformers (BERT). BERT's bidirectional Transformer architecture pre-trained utilizes Next Sentence Prediction (NSP) and Masked Language Modeling (MLM) and has achieved a lot in terms of AI transformation. This paper provides a description of the BERT design, pre-training methods, and fine-tuning for sentiment analysis tasks. The study goes ahead and compares BERT's performance with other deep learning models, machine learning algorithms, and traditional rule-based techniques, highlighting the latter's limited ability to handle linguistic nuances and context. Additionally, studies proving the consistency and accuracy of BERT's sentiment analysis are examined, along with the challenges of handling irony, sarcasm, and domain-specific data. Ethical and privacy concerns that sentiment analysis inherently raises and makes recommendations for further research are also examined in the study, which also shows how integrating sentiment analysis with other domains can lead to multidisciplinary breakthroughs that can offer more comprehensive insights and applications.
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Ashwini Bari, Rajeev Yadav. "Analysis of Product Review Sentiment Analysis using Improved Machine Learning Techniques." Kuwait Journal of Computer Science 1, no. 1 (2023): 30–37. http://dx.doi.org/10.52783/kjcs.v1i1.232.
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Sentiment analysis has emerged as a crucial task in the era of big data and social media. Understanding the sentiments expressed in product reviews is vital for businesses to gauge customer satisfaction and make informed decisions. This research paper presents a design simulation and assessment of product review sentiment analysis using improved machine learning techniques. The aim is to develop a robust sentiment analysis model that outperforms existing approaches in accuracy and efficiency. We propose a novel methodology that combines advanced feature extraction, sentiment classification algorithms, and model optimization techniques. The introduction provides an overview of the importance of sentiment analysis in the context of product reviews and the challenges faced by conventional methods. It also outlines the objectives and scope of this research. The related works section presents a comprehensive review of existing literature and highlights the limitations of current approaches. The proposed methodology section describes the technical details of our enhanced machine learning approach and the reasoning behind the selected techniques. In the analysis of sample results, we evaluate the performance of our proposed model on a diverse dataset of product reviews. We present the accuracy, precision, recall, and F1-score metrics, along with a comparison to baseline models and state-of-the-art sentiment analysis systems. Furthermore, we discuss the model's robustness in handling various types of products and reviews.
 Our research demonstrates significant improvements in sentiment analysis accuracy compared to traditional methods. We introduce tables and graphs to illustrate the model's performance in different scenarios and identify its strengths and weaknesses. The paper concludes by discussing the implications of our findings, potential applications in industry, and directions for future research. Overall, this research contributes to the advancement of sentiment analysis techniques and provides a valuable resource for businesses aiming to enhance their understanding of customer sentiments through product reviews.
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Sharma Vishalkumar Sureshbhai and Dr. Tulsidas Nakrani. "A Literature Review : Enhancing Sentiment Analysis of Deep Learning Techniques Using Generative AI Model." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 530–40. http://dx.doi.org/10.32628/cseit24103204.
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Sentiment analysis is possibly one of the most desirable areas of study within Natural Language Processing (NLP). Generative AI can be used in sentiment analysis through the generation of text that reflects the sentiment or emotional tone of a given input. The process typically involves training a generative AI model on a large dataset of text examples labeled with sentiments (positive, negative, neutral, etc.). Once trained, the model can generate new text based on the learned patterns, providing an automated way to analyze sentiments in user reviews, comments, or any other form of textual data. The main goal of this research topic is to identify the emotions as well as opinions of users or customers using textual means. Though a lot of research has been done in this area using a variety of models, sentiment analysis is still regarded as a difficult topic with a lot of unresolved issues. Slang terms, novel languages, grammatical and spelling errors, etc. are some of the current issues. This work aims to conduct a review of the literature by utilizing multiple deep learning methods on a range of data sets. Nearly 21 contributions, covering a variety of sentimental analysis applications, are surveyed in the current literature study. Initially, the analysis looks at the kinds of deep learning algorithms that are being utilized and tries to show the contributions of each work. Additionally, the research focuses on identifying the kind of data that was used. Additionally, each work's performance metrics and setting are assessed, and the conclusion includes appropriate research gaps and challenges. This will help in identifying the non-saturated application for which sentimental analysis is most needed in future studies.
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Jin, Yuxin, Kui Cheng, Xinjie Wang, and Lecai Cai. "A Review of Text Sentiment Analysis Methods and Applications." Frontiers in Business, Economics and Management 10, no. 1 (2023): 58–64. http://dx.doi.org/10.54097/fbem.v10i1.10171.
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This paper reviews the application of natural language processing in sentiment analysis. Sentiment analysis is an important task aimed at automatically identifying and inferring sentiment tendencies and sentiment intensity in texts. This paper first introduces the application areas of sentiment analysis, including practical applications of text sentiment analysis. Then, text pre-processing techniques such as word separation, deactivation removal and punctuation processing are discussed. Then, feature extraction and representation methods are explored, including bag-of-words model, TF-IDF, word embedding and Word2Vec, attention mechanism and Transformer. In addition, methods for sentiment analysis, such as sentiment dictionaries and rule-based methods, traditional machine learning methods, and deep learning-based methods, are presented. Finally, the application areas of sentiment analysis are discussed and conclusions are given. The review in this paper will help readers understand the current status and development trend of natural language processing applications in sentiment analysis, as well as the advantages and disadvantages of different methods in sentiment analysis.
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Zore, Soniya, Amol Bhosale, and Pratibha Chavan. "Sentiment Analysis." International Journal of Electronics and Computer Applications 1, no. 2 (2024): 50–54. https://doi.org/10.70968/ijeaca.v1i2.8.
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Social media platforms like Twitter have become a rich source of real-time data and public sentiment. Analysing sentiment on Twitter is essential for various applications, from brand monitoring to political analysis. This work focuses on Twitter sentiment analysis, employing natural language processing (NLP) techniques to categorize tweets as positive, negative, or neutral. We collect a large dataset of tweets, pre-process the text, and train machine learning models to predict sentiment. Our goal is to provide insights into public sentiment on various topics, trends, and events, which can be valuable for decision-makers in diverse domains.
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Zheng, Ruohan, Yile Li, and Yicheng Wang. "Traditional CAPM Model to Sentiment Analysis." Advances in Economics, Management and Political Sciences 81, no. 1 (2024): 58–64. http://dx.doi.org/10.54254/2754-1169/81/20241843.
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In this article, we will combine the novel applications of asset pricing, which is developed from a classic theory of this field. We will present this theory and then the latest found of the application. A large part will be the results based on the classic model CAPM. This part includes a review of Then present the other models or methods based on this result. Sentiment analysis is a vital component in stock price prediction, utilizing natural language processing and machine learning to extract and evaluate emotional information from textual data. This article reviews traditional lexicon-based methods and advanced sentiment analysis techniques, emphasizing the growing importance of sentiment and neural networks for a more nuanced understanding of sentiment's impact on financial markets. Integrating sentiment from social media, financial news, earnings reports, and analyst opinions provides a holistic view of market sentiment.
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Winardi, Sugeng, Mohammad Diqi, Arum Kurnia Sulistyowati, and Jelina Imlabla. "Sentiment Analysis of ChatGPT Tweets Using Transformer Algorithms." Jurnal Informatika dan Rekayasa Perangkat Lunak 5, no. 2 (2023): 113. http://dx.doi.org/10.36499/jinrpl.v5i2.8632.
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This study explores the application of the Transformer model in sentiment analysis of tweets generated by ChatGPT. We used a Kaggle dataset consisting of 217,623 instances labeled as "Good", "Bad", and "Neutral". The Transformer model demonstrated high accuracy (90%) in classifying sentiments, particularly predicting "Bad" tweets. However, it showed slightly lower performance for the "Good" and "Neutral" categories, indicating areas for future research and model refinement. Our findings contribute to the growing body of evidence supporting deep learning methods in sentiment analysis and underscore the potential of AI models like Transformers in handling complex natural language processing tasks. This study broadens the scope for AI applications in social media sentiment analysis.
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Chundi, Ramesh, Vishwanath R. Hulipalled, and Jay Bharthish Simha. "Lexicon-based sentiment analysis for Kannada-English code-switch text." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1500. http://dx.doi.org/10.11591/ijai.v12.i3.pp1500-1507.
Full textAbstract:
Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic, product, etc. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing (NLP) and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning (DL), machine learning (ML), or a Lexicon-based approach. Machine learning (ML) and deep learning (DL) methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with ML and DL. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better accuracy of 83.2% and 83% of F1-score.
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Tanveer, Asim, Mohibullah Khan, Rehan Sarwar, Naeem Aslam, and Muhammad Fuzail. "A Fine Grained Sentiment Analysis of Arabic Language." VAWKUM Transactions on Computer Sciences 12, no. 2 (2024): 178–90. https://doi.org/10.21015/vtcs.v12i2.1926.
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This work focuses on fine-grained sentiment analysis of Arabic text using recent Natural Language Processing methods. Arabic is a language rich in variation, spoken by over 400 million people, yet there is a significant lack of resources for sentiment analysis. To address these challenges, this study employs AraBERT, a model specifically fine-tuned for Arabic text. A corpus of one hundred thousand Arabic reviews across categories such as hotels, books, and movies was scraped and cleaned. These reviews were then categorized into positive, negative, and mixed sentiments. AraBERT was compared with traditional machine learning methods, including Logistic Regression, Decision Tree, Naïve Bayes, and Random Forest. AraBERT achieved superior accuracy of 88\%, along with higher precision, recall, and F1 scores for both positive and negative sentiment classes compared to the other models. This work demonstrates that AraBERT effectively analyzes the syntactic and semantic structure of Arabic, making it a valuable tool for Arabic sentiment analysis across various applications. Future work will extend the model to handle neutral sentiments and include additional dialects to further improve its performance.
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Chanaa, Abdessamad, and Nour-eddine El Faddouli. "Sentiment Analysis on Massive Open Online Courses (MOOCs)." International Journal of Information and Communication Technology Education 18, no. 1 (2022): 1–22. http://dx.doi.org/10.4018/ijicte.310004.
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Massive open online courses (MOOCs) have evolved rapidly in recent years due to their open and massive nature. However, MOOCs suffer from a high dropout rate, since learners struggle to stay cognitively and emotionally engaged. Learner feedback is an excellent way to understand learner behaviour and model early decision making. In the presented study, the authors aim to explore learner sentiment expressed in their comments using machine learning and multi-factor analysis methods. They address several research questions on sentiment analysis on educational data. A total of 3311 messages, posted on a MOOC discussion forum, were analysed and categorized using machine learning and data analysis. The results obtained in this study show that it is possible to perform sentiment analysis with very high accuracy (94.1%), and it is also possible to periodically supervise the variations in learners' sentiments. The results of this study are very useful. In the context of online learning, it is very beneficial to have information about learner sentiment.
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Pathak, Uddhav, and Er Piyush Rai. "Sentiment Analysis: Methods, Applications, and Future Directions." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 1453–58. http://dx.doi.org/10.22214/ijraset.2023.49165.
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Abstract: Sentiment analysis is a rapidly evolving field that aims to automatically identify and extract subjective information from text data. In recent years, sentiment analysis has gained widespread attention due to its potential applications in various domains, such as marketing, social media analysis, and customer feedback analysis. In this review paper, we provide a comprehensive analysis of sentiment analysis techniques, including traditional rule-based methods, machine learning-based methods, and deep learning-based methods. We discuss the advantages and limitations of these methods and compare their performance in various settings. Furthermore, we examine the challenges and opportunities in sentiment analysis research and present future directions for the field. Overall, this review aims to provide a critical assessment of sentiment analysis techniques, applications, and future developments, and to assist researchers and practitioners in understanding the state-of-the-art in this important area of natural language processing
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Ramswamy, Yogesh. "Classifying User Reviews of Movie Applications using Improved Logistic Regression." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1786–97. https://doi.org/10.22214/ijraset.2025.68593.
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Abstract: In recent years review classification, analysis and prediction are one of the most commonapplications of sentiment analysis. It involves detection of sentiments on the reviews made bythe users on social networking applications through opinion mining.In general,reviews canhave positive, negative or neutral polarity indicators. For classification, the polarity indicatorstake the form of certain words and emotions that readily show the user’s sentiments. Existingworks fall short of producing accurate classification results because of two-class problem thataffects the performance of evaluation parameters like precision, recall, accuracy and F-measure.Hencethereisaneedofanefficientclassificationtechniquewhichaddressestwo-classproblem. Thiswork proposes ImprovedversionofLogisticRegression[ILR]thatiscommonly used for sentiment analysis and classification. The proposed classification techniqueidentifies and replaces the misspelled words in the sentence,supportcountestimation andclassificationofreviewsalongwithmultipleindependentwordswithsimilarmeaninginparallel. The experimental results show the classification accuracy of the proposed technique tobemoreaccuratecomparedtothe existinglogistic regressionandnaïvebayesclassifiers.
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Anbu Durai, Srinaath, and Wang Zhaoxia. "Sentiment Analysis, Social Media and Urban Economics: The Case of Singaporean HDB and Covid-19." International Journal of Innovation and Economic Development 9, no. 5 (2023): 28–39. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.95.2003.
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Twitter sentiment analysis has been employed as a prognostic tool for predicting prices and trends in both stock and housing markets. Early studies in this domain drew inspiration from behavioural economics, establishing a link between sentiments or emotions and economic decision-making. However, recent investigations in this field have shifted their focus from the data utilized to the algorithms employed. A comprehensive literature review, with an emphasis on the data aspect, reveals a scarcity of research considering the influence of sentiments arising from external factors on stock or housing markets, despite abundant evidence in behavioural economics suggesting that sentiments induced by external factors impact economic decisions. To bridge this gap, this study explores the impact of Twitter sentiment related to the Covid-19 pandemic on housing prices in Singapore. Employing SNSCRAPE for tweet collection, sentiment analysis is conducted using VADER. Granger Causality is applied to investigate the relationship between Covid-19 cases and sentiment, while neural networks serve as prediction models. The research compares the predictive capacity of Twitter sentiment regarding Covid-19 with traditional housing price predictors, such as structural and neighbourhood characteristics. Findings indicate that utilizing Twitter sentiment related to Covid-19 yields superior predictions compared to relying solely on traditional predictors, outperforming two specific traditional predictors. Consequently, this study underscores the significance of considering Twitter sentiment related to external factors as crucial in economic predictions, demonstrating practical applications of sentiment analysis on Twitter data in real-world economic scenarios.
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Wu, Lifang, Sinuo Deng, Heng Zhang, and Ge Shi. "Sentiment Interaction Distillation Network for Image Sentiment Analysis." Applied Sciences 12, no. 7 (2022): 3474. http://dx.doi.org/10.3390/app12073474.
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Sentiment is a high-level abstraction, and it is a challenging task to accurately extract sentimental features from visual contents due to the “affective gap”. Previous works focus on extracting more concrete sentimental features of individual objects by introducing saliency detection or instance segmentation into their models, neglecting the interaction among objects. Inspired by the observation that interaction among objects can impact the sentiment of images, we propose the Sentiment Interaction Distillation (SID) Network, which utilizes object sentimental interaction to guide feature learning. Specifically, we first utilize a panoptic segmentation method to obtain objects in images; then, we propose a sentiment-related edge generation method and employ Graph Convolution Network to aggregate and propagate object relation representation. In addition, we propose a knowledge distillation framework to utilize interaction information guiding global context feature learning, which can avoid noisy features introduced by error propagation and a varying number of objects. Experimental results show that our method outperforms the state-of-the-art algorithm, e.g., about 1.2% improvement on the Flickr dataset and 1.7% on the most challenging subset of Twitter I. It is demonstrated that the reasonable use of interaction features can improve the performance of sentiment analysis.
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Água, Mariana, Nuno António, Paulo Carrasco, and Carimo Rassal. "Large Language Models Powered Aspect-Based Sentiment Analysis for Enhanced Customer Insights." Tourism & Management Studies 21, no. 1 (2025): 1–19. https://doi.org/10.18089/tms.20250101.
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In the age of social networks, user-generated content has become vital for organizations in tourism and hospitality. Traditional sentiment analysis methods often struggle to process large volumes of data and capture implicit sentiments. This study examines the potential of Aspect-Based Sentiment Analysis (ABSA) using Large Language Models (LLMs) to enhance sentiment analysis. By employing GPT-4o via ChatGPT, we benchmark three approaches: a fuzzy logic-based method, manual human analysis, and a new ChatGPT-based analysis. We analyze a dataset of 500 all-inclusive hotel reviews, comparing these methods to assess ChatGPT's effectiveness in identifying nuanced language and handling subjectivity. The findings reveal a high similarity between ChatGPT and human analysis, showcasing ChatGPT’s ability to interpret complex sentiments and automate sentiment classification tasks. This study highlights the potential of LLMs in transforming customer feedback analysis, providing deeper insights, and improving responsiveness in the hospitality industry. These results contribute to academia by presenting a framework for using LLMs in ABSA and guiding future applications and development.
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Victoria, Dr Helen. "Real-Time Multi-Class Sentiment Analysis of Social Media Threads: A Deep Learning Approach." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 3376–89. https://doi.org/10.22214/ijraset.2025.68069.
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Social media websites create enormous amounts of user-generated content, and hence sentiment analysis is a valuable tool in order to know the public opinions, trends, and emotions. Conventional methods of sentiment analysis are difficult to deal with real-time processing and multi-class classification because social media threads are dynamic and unstructured in nature. This work suggests a deep learning type of method for real time multi-class sentiment analysis of social media posts. This framework uses transformer-based architectures like BERT and LSTM networks to accurately categorize sentiments into different categories and even finer emotions. The framework is capable of dealing with the hierarchical and conversational nature of social media threads and is scalable and fast enough for real-time applications. Experimental outcomes on benchmark datasets prove the strength of our method in terms of obtaining high accuracy as well as stability. Our work advances sentiment analysis through the integration of deep learning methods for improved contextual comprehension and real-time processing, which makes it useful for market analysis, political sentiment monitoring, and crisis management applications.