A Comprehensive Overview of Sentiment Analysis: Evolution, Approaches, and Applications
DOI:
https://doi.org/10.13052/jgeu0975-1416.1422Keywords:
Sentiment analysis, subjectivity, polarity, support vector machine, NaiveBayesAbstract
Sentiment analysis (SA) also known as opinion mining is a tool to determine the sentiments associated with linguistic data and is used by a variety of organisations over various domain. SA analysis has proved to be an effective tool at various business levels, organisational levels etc. Big companies, organisations, governments use social media platforms to identify the sentiments of the target audience in order to enhance their services and products. Current study is a chronological survey of SA, its applications and various approaches that were developed over the time for SA. This paper focuses on various SA techniques developed and their real-world applications. Details of SA, types of SA, terminology and approaches related and developed overtime are discussed at length. Applications and works related SA in domains related to social media, big business, and political campaigns are also discussed.
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Abd El-Jawad, M. H., Hodhod, R., and Omar, Y. M. (2018, December). Sentiment analysis of social media networks using machine learning. In 2018 14th international computer engineering conference (ICENCO) (pp. 174–176). IEEE.
Abdou, A. L. N. N., and Tagne, E. F. (2021, December). A Sentiment Analysis Approach for Abusive Content Detection using Improved Dataset. In 2021 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1415–1420). IEEE.
Abdulla, N. A., Ahmed, N. A., Shehab, M. A., and Al-Ayyoub, M. (2013, December). Arabic sentiment analysis: Lexicon-based and corpus-based. In 2013 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT) (pp. 1–6). IEEE.
Adak, A., Pradhan, B., and Shukla, N. (2022). Sentiment analysis of customer reviews of food delivery services using deep learning and explainable artificial intelligence: Systematic review. Foods, 11(10), 1500.
Agarwal, B., and Mittal, N. (2013, June). Sentiment classification using rough set based hybrid feature selection. In Proceedings of the 4th workshop on computational approaches to Subjectivity, sentiment and social media analysis (pp. 115–119).
Agarwal, B., Mittal, N., Bansal, P., and Garg, S. (2015). Sentiment analysis using common-sense and context information. Computational intelligence and neuroscience, 2015, 30–30.
Al Amrani, Y., Lazaar, M., and El Kadiri, K. E. (2018). A novel hybrid classification approach for sentiment analysis of text document. Int. J. Electr. Comput. Eng, 8(6), 4554–4567.
AlBadani, Barakat, Shi, Ronghua and Dong, Jian. (2022). A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM. Applied System Innovation. 5. 13. DOI: 10.3390/asi5010013.
Alharbi, J. R., and Alhalabi, W. S. (2020). Hybrid approach for sentiment analysis of twitter posts using a dictionary-based approach and fuzzy logic methods: Study case on cloud service providers. International Journal on Semantic Web and Information Systems (IJSWIS), 16(1), 116–145.
Alqaryouti, O., Siyam, N., Abdel Monem, A., and Shaalan, K. (2024). Aspect-based sentiment analysis using smart government review data. Applied Computing and Informatics, 20(1/2), 142–161.
Alshuwaier, F., Areshey, A., and Poon, J. (2022). Applications and enhancement of document-based sentiment analysis in deep learning methods: Systematic literature review. Intelligent Systems with Applications, 15, 200090.
Altaher, A. (2017). Hybrid approach for sentiment analysis of Arabic tweets based on deep learning model and features weighting. Int. J. Adv. Appl. Sci, 4(8), 43–49.
Appel, O., Chiclana, F., Carter, J., and Fujita, H. (2016). A hybrid approach to the sentiment analysis problem at the sentence level. Knowledge-Based Systems, 108, 110–124.
Appel, O., Chiclana, F., Carter, J., and Fujita, H. (2018). Successes and challenges in developing a hybrid approach to sentiment analysis. Applied Intelligence, 48, 1176–1188.
Aqlan, A. A. Q., Manjula, B., and Lakshman Naik, R. (2019). A study of sentiment analysis: concepts, techniques, and challenges. In Proceedings of International Conference on Computational Intelligence and Data Engineering: Proceedings of ICCIDE 2018 (pp. 147–162). Springer Singapore.
Babu, N. V., and Kanaga, E. G. M. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN computer science, 3(1),
Baishya, D., Deka, J. J., Dey, G., and Singh, P. K. (2021). SAFER: sentiment analysis-based fake review detection in e-commerce using deep learning. SN Computer Science
Bakliwal, A., Foster, J., van der Puil, J., O’Brien, R., Tounsi, L., and Hughes, M. (2013, June). Sentiment analysis of political tweets: Towards an accurate classifier. Association for Computational Linguistics.
Basiri, M. E., and Kabiri, A. (2017, October). Translation is not enough: comparing lexicon-based methods for sentiment analysis in Persian. In 2017 international symposium on computer science and software engineering conference (CSSE) (pp. 36–41). IEEE
Begg, R., and Kamruzzaman, J. (2005). A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. Journal of biomechanics, 38(3), 401–408.
Beigi, G., Hu, X., Maciejewski, R., and Liu, H. (2016). An overview of sentiment analysis in social media and its applications in disaster relief. Sentiment analysis and ontology engineering: An environment of computational intelligence, 313–340.
Cao Q, Duan W, Gan Q (2011) Exploring determinants of voting for the “helpfulness” of online user reviews: a text mining approach. Decis Support Syst 50(2):511–521
Catelli, R., Pelosi, S., Comito, C., Pizzuti, C., and Esposito, M. (2023). Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy. Computers in Biology and Medicine, 158, 106876.
Chathuranga, P. D. T., Lorensuhewa, S. A. S., and Kalyani, M. A. L. (2019, September). Sinhala sentiment analysis using corpus based sentiment lexicon. In 2019 19th international conference on advances in ICT for emerging regions (ICTer) (Vol. 250, pp. 1–7). IEEE.
Chau, M., and Chen, H. (2008). A machine learning approach to web page filtering using content and structure analysis. Decision Support Systems, 44(2), 482–494.
Chauhan, G. S., Nahta, R., Meena, Y. K., and Gopalani, D. (2023). Aspect based sentiment analysis using deep learning approaches: A survey. Computer Science Review, 49, 100576.
Chauhan, P., Sharma, N., and Sikka, G. (2021). The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing, 12, 2601–2627.
Che, W., Zhao, Y., Guo, H., Su, Z., and Liu, T. (2015). Sentence compression for aspect-based sentiment analysis. IEEE/ACM Transactions on audio, speech, and language processing, 23(12), 2111–2124.
Ciftci, B., and Apaydin, M. S. (2018, September). A deep learning approach to sentiment analysis in Turkish. In 2018 international conference on artificial intelligence and data processing (IDAP) (pp. 1–5). IEEE.
Dave, K., Lawrence, S., and Pennock, D. M. (2003, May). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web (pp. 519–528).
De Arruda, H. F., Marinho, V. Q., Costa, L. D. F., and Amancio, D. R. (2019). Paragraph-based representation of texts: A complex networks approach. Information Processing & Management, 56(3), 479–494.
De Clercq, O., Lefever, E., Jacobs, G., Carpels, T., and Hoste, V. (2017, September). Towards an integrated pipeline for aspect-based sentiment analysis in various domains. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 136–142).
Devlin, J., Chang, M. W., Lee, K., and Toutanova, K. (2019, June). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 4171–4186).
Dewi, T. B. T., Indrawan, N. A., Budi, I., Santoso, A. B., and Putra, P. K. (2020, September). Community understanding of the importance of social distancing using sentiment analysis in Twitter. In 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE) (pp. 336–341). IEEE.
Dhaoui, C., Webster, C. M., and Tan, L. P. (2017). Social media sentiment analysis: lexicon versus machine learning. Journal of Consumer Marketing, 34(6), 480–488.
El-Ansari, A., and Beni-Hssane, A. (2023). Sentiment analysis for personalized chatbots in e-commerce applications. Wireless Personal Communications, 129(3), 1623–1644.
Ferguson, P., O’Hare, N., Davy, M., Bermingham, A., Sheridan, P., Gurrin, C., and Smeaton, A. F. (2009). Exploring the use of paragraph-level annotations for sentiment analysis of financial blogs.
Ghorbani, M., Bahaghighat, M., Xin, Q., and Özen, F. (2020). ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing. Journal of Cloud Computing, 9(1), 16.
Gonçalves, P., Araújo, M., Benevenuto, F., and Cha, M. (2013, October). Comparing and Combining sentiment analysis methods. In Proceedings of the first ACM conference on online social networks (pp. 27–38).
Govindarajan, M. (2014). Sentiment classification of movie reviews using hybrid method. International Journal of Advances in Science Engineering and Technology, 1(3), 73–77.
Grljević, O., Bošnjak, Z., and Kovačević, A. (2022). Opinion mining in higher education: a corpus-based approach. Enterprise Information Systems, 16(5), 1773542.
Hajek, P., Hikkerova, L., and Sahut, J. M. (2023). Fake review detection in e-Commerce platforms using aspect-based sentiment analysis. Journal of Business Research, 167, 114143.
Hassan, A., and Mahmood, A. (2017, April). Deep learning approach for sentiment analysis of short texts. In 2017 3rd international conference on control, automation and robotics (ICCAR) (pp. 705–710). IEEE.
Hausler, J., Ruscheinsky, J. and Lang, M. (2018) ’News-based sentiment analysis in real estate: a machine learning approach’, Journal of Property Research, 35(4), pp. 344–371. DOI: 10.1080/09599916.2018.1551923.
Hochreiter, S., and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Hossain, M. S., and Rahman, M. F. (2023). Customer sentiment analysis and prediction of insurance products’ reviews using machine learning approaches. FIIB Business Review, 12(4), 386–402.
Hovy, E. H. (2015). What are sentiment, affect, and emotion? Applying the methodology of Michael Zock to sentiment analysis. Language production, cognition, and the Lexicon, 13–24.
Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330–338.
Iqbal, F., Hashmi, J. M., Fung, B. C., Batool, R., Khattak, A. M., Aleem, S., and Hung, P. C. (2019). A hybrid framework for sentiment analysis using genetic algorithm based feature reduction. IEEE Access, 7, 14637–14652.
Isah, H., Trundle, P., and Neagu, D. (2014, September). Social media analysis for product safety using text mining and sentiment analysis. In 2014 14th UK workshop on computational intelligence (UKCI) (pp. 1–7). IEEE.
Jagdale, R. S., Shirsat, V. S., and Deshmukh, S. N. (2019). Sentiment analysis on product reviews using machine learning techniques. In Cognitive Informatics and Soft.
Jurek, A., Mulvenna, M. D., and Bi, Y. (2015). Improved lexicon-based sentiment analysis for social media analytics. Security Informatics, 4, 1–13.
Kabir, M., Kabir, M. M. J., Xu, S., and Badhon, B. (2021). An empirical research on sentiment analysis using machine learning approaches. International Journal of Computers and Applications, 43(10), 1011–1019.
Kanayama, H., and Nasukawa, T. (2006, July). Fully automatic lexicon expansion for domain-oriented sentiment analysis. In Proceedings of the 2006 conference on empirical methods in natural language processing (pp. 355–363).
Kang, D., and Park, Y. (2014). Based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41(4), 1041–1050.
Karlgren, J., and Cutting, D. (1994). Recognizing text genres with simple metrics using discriminant analysis. arXiv preprint cmp-Ig/941 0008.
Kaur, G., and Sharma, A. (2023). A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. Journal of big data, 10(1), 5.
Kaur, J., and Sidhu, B. K. (2018, June). Sentiment analysis based on deep learning approaches. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1496–1500). IEEE.
Ke, W., and Chan, K. H. (2021). A multilayer CARU framework to obtain probability distribution for paragraph-based sentiment analysis. Applied Sciences, 11(23), 11344.
Kessler, B., Nunberg, G., and Schütze, H. (1997). Automatic detection of text genre. arXiv preprint cmplg/9707002.
Khan, A., Baharudin, B., and Khan, K. (2010, December). Sentence based sentiment classification from online customer reviews. In Proceedings of the 8th International Conference on Frontiers of Information Technology (pp. 1–6).
Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
Kumar, A., Setia, S., Singh, A., Abraham, T., and Shakya, Y. (2022). Sentimental Analysis using Product Review Data. Telecom Business Review, 15(1), 11.
Kumar, P., and Jaiswal, U. C. (2016). A comparative study on sentiment analysis and opinion mining. Int J Eng Technol, 8(2), 938–943.
Kyaw, K. S., Tepsongkroh, P., Thongkamkaew, C., and Sasha, F. (2023). Business intelligent framework using sentiment analysis for smart digital marketing in the E-commerce era. Asia Social Issues, 16(3), e252965–e252965.
Lagrari, F. E., and Elkettani, Y. (2021). Traditional and deep learning approaches for sentiment analysis: A survey. Advances in Science, Technology and Engineering Systems Journal, 6(4), 1–7.
Letarte, G., Paradis, F., Giguère, P., and Laviolette, F. (2018, November). Importance of self-attention for sentiment analysis. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (pp. 267–275).
Li, S. (2013). Sentiment classification using subjective and objective views. International Journal of Computer Applications, 80(7).
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., … and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
Lubis, A. R., Nasution, M. K., Sitompul, O. S., and Zamzami, E. M. (2020, July). A framework of utilizing big data of social media to find out the habits of users using keyword. In Proceedings of the 8th International Conference on Computer and Communications Management (pp. 140–144).
Ma, Y., Peng, H., Khan, T., Cambria, E., and Hussain, A. (2018). Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cognitive Computation, 10, 639–650.
Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, Manfred Stede; Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics 2011; 37(2): 267–307.
Malandrakis, N., Kazemzadeh, A., Potamianos, A., and Narayanan, S. (2013, June). SAIL: A hybrid approach to sentiment analysis. In Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (pp. 438–442).
Marong, M., Batcha, N. K., and Mafas, R. (2020). Sentiment Analysis in E-Commerce: A Review on The Techniques and Algorithms. Journal of Applied Technology and Innovation (e-ISSN: 2600–7304), 4(1), 6.
Maynard, D., Dupplaw, D., and Hare, J. (2013). Multimodal sentiment analysis of social media.
Melville, P., Gryc, W., and Lawrence, R. D. (2009, June). Sentiment analysis of blogs by combining lexical knowledge with text classification. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1275–1284).
Mendon, S., Dutta, P., Behl, A., and Lessmann, S. (2021). A hybrid approach of machine learning and lexicons to sentiment analysis: Enhanced insights from twitter data of natural disasters. Information Systems Frontiers, 23(5), 1145–1168.
Mittal, N., Agarwal, B., Agarwal, S., Agarwal, S., and Gupta, P. (2013). A hybrid approach for twitter sentiment analysis. In 10th international conference on NLP (ICON-2013) (pp. 116–120).
Mittal, S., Goel, A., and Jain, R. (2016, March). Sentiment analysis of E-commerce and social networking sites. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIA Com) (pp. 2300–2305). IEEE.
Moghaddam, S. (2015). Beyond sentiment analysis: mining defects and improvements from customer feedback. In Advances in Information Retrieval: 37th European Conference on IR Research, ECIR 2015, Vienna, Austria, March 29–April 2, 2015. Proceedings 37 (pp. 400–410). Springer International Publishing.
Montoyo, A., Martínez-Barco, P., and Balahur, A. (2012). Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments. Decision Support Systems, 53(4), 675–679.
Moreno-Ortiz, A., and Fernández-Cruz, J. (2015). Identifying polarity in financial texts for sentiment analysis: a corpus-based approach. Procedia-Social and Behavioral Sciences, 198, 330–338.
Morinaga, S., Yamanishi, K., Tateishi, K., and Fukushima, T. (2002, July). Mining product reputations on the web. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 341–349).
Muhammad, A., Wiratunga, N., and Lothian, R. (2016). Contextual sentiment analysis for social media genres. Knowledge-based systems, 108, 92–101.
Mukwazvure, A., and Supreethi, K. P. (2015, September). A hybrid approach to sentiment analysis of news comments. In 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions) (pp. 1–6). IEEE.
Mullen, T., and Collier, N. (2004, July). Sentiment analysis using SVM with diverse information sources. In Proceedings of the 2004 conference on empirical methods in NLP (pp. 412–418).
Müller, K. R., Tangermann, M., Dornhege, G., Krauledat, M., Curio, G., and Blankertz, B. (2008). Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. Journal of neuroscience methods, 167(1), 82–90.
Nadeau, D., Sabourin, C., De Koninck, J., Matwin, S., and Turney, P. D. (2006, July). Automatic dream sentiment analysis. In Proceedings of the Workshop on Computational Aesthetics at the Twenty-First National Conference on Artificial Intelligence.
Nandi, V., and Agrawal, S. (2016). Political sentiment analysis using hybrid approach. International Research Journal of Engineering and Technology, 3(5), 1621–1627.
Nanli, Z., Ping, Z., Weiguo, L. I., and Meng, C. (2012, November). Sentiment analysis: A Literature review. In 2012 International Symposium on Management of Technology (ISMOT) (pp. 572–576). IEEE.
Nasukawa, T., and Yi, J. (2003, October). Sentiment analysis: Capturing favorability using NLP. In Proceedings of the 2nd international conference on Knowledge capture (pp. 70–77).
Nazir, A., Rao, Y., Wu, L., and Sun, L. (2020). Issues and challenges of aspect-based sentiment analysis: A comprehensive survey. IEEE Transactions on Affective Computing, 13(2), 845–863.
Neethu, M. S., and Rajasree, R. (2013, July). Sentiment analysis in twitter using machine learning techniques. In 2013 fourth international conference on computing, communications and networking technologies (ICCCNT) (pp. 1–5). IEEE.
Neri, F., Aliprandi, C., Capeci, F., and Cuadros, M. (2012, August). Sentiment analysis on social media. In 2012 IEEE/ACM international conference on advances in social networks analysis and mining (pp. 919–926). IEEE.
Noor, A., and Islam, M. (2019, July). Sentiment Analysis for Women’s E-commerce Reviews using Machine Learning Algorithms. In 2019 10th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1–6).
Noor, A., and Islam, M. (2019, July). Sentiment Analysis for Women’s E-commerce Reviews using Machine Learning Algorithms. In 2019 10th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1–6).
O’Hare, N., Davy, M., Bermingham, A., Ferguson, P., Sheridan, P., Gurrin, C., and Smeaton, A. F. (2009, November). Topic-dependent sentiment analysis of financial blogs. In Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion (pp. 9–16).
Onyenwe, I., Nwagbo, S., Mbeledogu, N., and Onyedinma, E. (2020). The impact of political party/candidate on the election results from a sentiment analysis perspective using# AnambraDecides2017 tweets. Social Network Analysis and Mining, 10, 1–17.
P.D. Turney, Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews, in: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, 2002, pp. 417–424.
Pandarachalil, R., Sendhilkumar, S., Mahalakshmi, G.S.: Twitter sentiment analysis for largescale data: an unsupervised approach. Cogn. Comput. 7(2), 254–262 (2015)
Pang, B., and Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in information retrieval, 2(1–2), 1–135.
Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070.
Park S, Kim Y (2016) Building thesaurus lexicon using dictionary-based approach for sentiment classifica tion. In: 2016 IEEE 14th international conference on software engineering research, management and applications (SERA), pp. 39–44.
Pavlopoulos, J., and Androutsopoulos, I. (2014, April). Multi-granular aspect aggregation in aspectbased sentiment analysis. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (pp. 78–87).
Pawar, A. B., Jawale, M. A., and Kyatanavar, D. N. (2016). Fundamentals of sentiment analysis: concepts and methodology. Sentiment analysis and ontology engineering: An environment of computational intelligence, 25–48.
Phan, M. H., and Ogunbona, P. O. (2020, July). Modelling context and syntactical features for aspectbased sentiment analysis. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 3211–3220).
Qi, B., and Costin, A. M. (2019, June). Investigation of the influence of Twitter user habits on sentiment of their opinions towards transportation services. In ASCE International Conference on Computing in Civil Engineering 2019 (pp. 314–321). Reston, VA: American Society of Civil Engineers.
Ramirez-Alcocer, U. M., Tello-Leal, E., Hernandez-Resendiz, J. D., and Romero, G. (2024). A Hybrid CNN-LSTM Approach for Sentiment Analysis. In Congress on Intelligent Systems (pp. 425–437). Springer, Singapore.
Ramteke, J., Shah, S., Godhia, D., and Shaikh, A. (2016, August). Election result prediction using Twitter sentiment analysis. In 2016 international conference on inventive computation technologies (ICICT) (Vol. 1, pp. 1–5). IEEE.
Rani, P., Liu, C., Sarkar, N., and Vanman, E. (2006). An empirical study of machine learning techniques for affect recognition in human-robot interaction. Pattern Analysis and Applications, 9, 58–69.
Rasappan, P., Premkumar, M., Sinha, G., and Chandrasekaran, K. (2024). Transforming sentiment analysis for e-commerce product reviews: Hybrid deep learning model with an innovative term weighting and feature selection. Information Processing & Management, 61(3), 103654.
Rathan, M., Hulipalled, V. R., Murugeshwari, P., and Sushmitha, H. M. (2017, August). Every post matters: a survey on applications of sentiment analysis in social media. In 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 709–714). IEEE.
Rice, D. R., and Zorn, C. (2021). Corpus-based dictionaries for sentiment analysis of specialized vocabularies. Political Science Research and Methods, 9(1), 20–35.
Rodríguez-Ibáñez, M., Gimeno-Blanes, F. J., Cuenca-Jiménez, P. M., Soguero-Ruiz, C., and Rojo-Álvarez, J. L. (2021). Sentiment analysis of political tweets from the 2019 Spanish elections. IEEE Access, 9, 101847–101862.
Ruder, S., Ghaffari, P., and Breslin, J. G. (2016). A hierarchical model of reviews for aspect-based sentiment analysis. arXiv preprint arXiv:1609.02745.
Sai Nikhita, N, Hyndavi, V, and Trupthi, M (2020). Detection of Inappropriate Anonymous Comments Using NLP and Sentiment Analysis. In Advances in Decision Sciences, Image Processing, Security and Computer Vision: International Conference on Emerging Trends in Engineering (ICETE), Vol. 1 (pp. 131–138). Springer International Publishing.
Salah, Z., Al-Ghuwairi, A. R. F., Baarah, A., Aloqaily, A., Qadoumi, B. A., Alhayek, M., and Alhijawi, B. (2019). A systematic review on opinion mining and sentiment analysis in social media. International Journal of Business Information Systems, 31(4), 530–554.
Sallam, R. M., Hussein, M., and Mousa, H. M. (2022). Improving collaborative filtering using lexiconbased sentiment analysis. International Journal of Electrical and Computer Engineering, 12(2), 1744.
Sandoval-Almazan, R., and Valle-Cruz, D. (2018, May). Facebook impact and sentiment analysis on political campaigns. In Proceedings of the 19th annual international conference on digital government research: governance in the data age (pp. 1–7).
Saroha, K., Sehrawat, M., and Jain, V. (2024). Machine Learning and Sentiment Analysis for Analyzing Customer Feedback: A Review. Big Data Analytics Techniques for Market Intelligence, 411–440.
Sharma, P., and Moh, T. S. (2016, December). Prediction of Indian election using sentiment analysis on Hindi Twitter. In 2016 IEEE international conference on big data (big data) (pp. 1966–1971). IEEE.
Sharma, S., and Jain, A. (2020). Role of sentiment analysis in social media security and analytics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5), e1366.
Singh, N. K., Tomar, D. S., and Sangaiah, A. K. (2020). Sentiment analysis: a review and comparative analysis over social media. Journal of Ambient Intelligence and Humanized Computing, 11(1), 97–117.
Singla, Z., Randhawa, S., and Jain, S. (2017, June). Sentiment analysis of customer product reviews using machine learning. In 2017 international conference on intelligent computing and control (I2C2) (pp. 1–5). IEEE.
Sunitha, D., Patra, R. K., Babu, N. V., Suresh, A., and Gupta, S. C. (2022). Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries. Pattern Recognition Letters, 158, 164–170.
Taj, S., Shaikh, B. B., and Meghji, A. F. (2019, January). Sentiment analysis of news articles: a lexicon based approach. In 2019 2nd international conference on computing, mathematics and engineering technologies (iCoMET) (pp. 1–5). IEEE.
Tan, K. L., Lee, C. P., Lim, K. M., and Anbananthen, K. S. M. (2022). Sentiment analysis with ensemble hybrid deep learning model. IEEE Access, 10, 103694–103704.
Tang, D. (2015, February). Sentiment-specific representation learning for document-level sentiment analysis. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 447452).
Tang, D., Qin, B., and Liu, T. (2015). Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(6), 292–303.
Trilla, A., and Alias, F. (2012). Sentence-based sentiment analysis for expressive text-to-speech. IEEE transactions on audio, speech, and language processing, 21(2), 223–233.
Tumasjan, A., Sprenger, T., Sandner, P., and Welpe, I. (2010, May). Predicting elections with twitter: What 140 characters reveal about political sentiment. In Proceedings of the international AAAI conference on web and social media (Vol. 4, No. 1, pp. 178–185).
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. arXiv preprint cs/0212032.
Turney, P. D., and Littman, M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. Acm Transactions on Information Systems (tois), 21(4), 315–346.
Rohani, V. A., and Shayaa, S. (2015, August). Utilizing machine learning in Sentiment Analysis: SentiRobo approach. In 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET) (pp. 263–267). IEEE.
Varghese, R., and Jayasree, M. (2013, August). Aspect based sentiment analysis using support vector machine classifier. In 2013 international conference on advances in computing, communications and informatics (ICACCI) (pp. 1581–1586). IEEE.
Vinodhini, G., and Chandrasekaran, R. M. (2012). Sentiment analysis and opinion mining: a Survey. International Journal, 2(6), 282–292.
Wang, C. J., Tsai, M. F., Liu, T., and Chang, C. T. (2013, October). Financial sentiment analysis for risk prediction. In Proceedings of the Sixth International Joint Conference on NLP (pp. 802–808).
Wang, J., Xu, B., and Zu, Y. (2021, July). Deep learning for aspect-based sentiment analysis. In 2021 international conference on machine learning and intelligent systems engineering (MLISE) (pp. 267–271). IEEE.
Whitelaw, C., Garg, N., and Argamon, S. (2005, October). Using appraisal groups for sentiment analysis. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 625–631).
Wiebe, J. (2000). Learning subjective adjectives from corpora. Aaai/iaai, 20(0), 0.
Wilson, T., Wiebe, J., and Hoffmann, P. (2005, October). Recognizing contextual polarity in phraselevel sentiment analysis. In Proceedings of human language technology conference and conference on empirical methods in NLP (pp. 347–354).
Wilson, T., Wiebe, J., and Hoffmann, P. (2009). Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational linguistics, 35(3), 399–433.
Wu, D. C., Zhong, S., Qiu, R. T., and Wu, J. (2022). Are customer reviews just reviews? Hotel forecasting using sentiment analysis. Tourism Economics, 28(3), 795–816.
Xu, H., Liu, B., Shu, L., and Yu, P. S. (2019). BERT post-training for review reading comprehension and aspect-based sentiment analysis. arXiv preprint arXiv:1904.02232.
Yang, L., Li, Y., Wang, J., and Sherratt, R. S. (2020). Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE access, 8, 23522–23530, IEEE.
Yu, J., Chen, K., and Xia, R. (2022). Hierarchical interactive multimodal transformer for aspect-based multimodal sentiment analysis. IEEE Transactions on Affective Computing, 14(3), 1966–1978.
Yue, L., Chen, W., Li, X., Zuo, W., and Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60, 617–663
Zaki Ahmed, A., and Rodríguez-Díaz, M. (2020). Significant labels in sentiment analysis of online customer reviews of airlines. Sustainability, 12(20), 8683.
Zhang, W., Li, X., Deng, Y., Bing, L., and Lam, W. (2022). A survey on aspect-based sentiment analysis: Tasks, methods, and challenges. IEEE Transactions on Knowledge and Data Engineering.
Zhao, G., Luo, Y., Chen, Q., and Qian, X. (2023). Aspect-based sentiment analysis via multitask learning for online reviews. Knowledge-Based Systems, 264, 110326.
Zhao, H., Liu, Z., Yao, X., and Yang, Q. (2021). A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach. Information Processing & Management, 58(5), 102656.
Zheng, L., Jin, P., Zhao, J., and Yue, L. (2014). Multi-dimensional sentiment analysis for large-scale Ecommerce reviews. In Database and Expert Systems Applications: 25th International Conference, DEXA 2014, Munich, Germany, September 1–4, 2014. Proceedings, Part II 25 (pp. 449–463). Springer International Publishing.
