Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
Source abstract
Abstract
The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across different platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper offers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses different neural network-based approaches for analyzing sentiment. Finally, these different approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Stock Market Forecasting Methods · Advanced Text Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632 014, Tamil Nadu, India
Department of Information Security Engineering, Soonchunhyang University, Asan 31538, Korea
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
Department of Electronic Engineering, University of Seville, 41092 Sevilla, Spain
Funding
Ministry of Education
References (100+)
Show source-provided references
1. Khan, A., Baharudin, B., and Khan, K. (2010, January 19–21). Efficient feature selection and domain relevance term weighting method for document classification. Proceedings of the 2010 Second International Conference on the Computer Engineering and Applications (ICCEA), Bali Island, Indonesia.10.1109/ICCEA.2010.228 ↗
2. Lu, Y., Kong, X., Quan, X., Liu, W., and Xu, Y. (2010). Exploring the sentiment strength of user reviews. International Conference on Web-Age Information Management, Springer.10.1007/978-3-642-14246-8_46 ↗
3. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., and Passonneau, R. Sentiment analysis of twitter data. Proceedings of the Workshop on Languages in Social Media.
4. Annett, M., and Kondrak, G. (2008). A comparison of sentiment analysis techniques: Polarizing movie blogs. Conference of the Canadian Society for Computational Studies of Intelligence, Springer.10.1007/978-3-540-68825-9_3 ↗
5. Sentiment analysis and opinion mining
6. Predicting Social Emotions from Readers’ Perspective10.1109/TAFFC.2017.2695607 ↗
7. Liu, B., Hu, M., and Cheng, J. (2020, January 25). Opinion observer: Analyzing and comparing opinions on the web. Proceedings of the 14th International Conference on World Wide Web, Cardiff, UK.
8. Tweet segmentation and its application to named entity recognition10.1109/TKDE.2014.2327042 ↗
9. A model for sentiment and emotion analysis of unstructured social media text10.1007/s10660-017-9257-8 ↗
10. Sentence compression for aspect-based sentiment analysis10.1109/TASLP.2015.2443982 ↗
11. Microblogging as a mechanism for human–robot interaction10.1016/j.knosys.2014.05.009 ↗
12. Polarity shift detection, elimination and ensemble: A three-stage model for document-level sentiment analysis10.1016/j.ipm.2015.04.003 ↗
13. Singh, V.K., Piryani, R., Uddin, A., and Waila, P. (2013, January 22–23). Sentiment analysis of Movie reviews and Blog posts. Proceedings of the 2013 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, India.10.1109/IAdCC.2013.6514345 ↗
14. Sentiment, emotion, purpose, and style in electoral tweets10.1016/j.ipm.2014.09.003 ↗
15. Studying emotion induced by music through a crowdsourcing game10.1016/j.ipm.2015.03.004 ↗
16. Sentiment analysis in medical settings: New opportunities and challenges10.1016/j.artmed.2015.03.006 ↗
17. Sentiment analysis algorithms and applications: A survey10.1016/j.asej.2014.04.011 ↗
18. Implicit aspect extraction in sentiment analysis: Review, taxonomy, oppportunities, and open challenges10.1016/j.ipm.2018.03.008 ↗
19. A survey of sentiment analysis in social media10.1007/s10115-018-1236-4 ↗
20. A survey of multimodal sentiment analysis10.1016/j.imavis.2017.08.003 ↗
21. Yue, L., Chen, W., Li, X., Zuo, W., and Yin, M. (2017, January 8–11). Survey on target dependent sentiment analysis of micro-blogs in social media. Proceedings of the 9th IEEE-GCC Conference and Exhibition, GCCCE, Manama, Bahrain.
22. Aspect extraction in sentiment analysis: Comparative analysis and survey10.1007/s10462-016-9472-z ↗
23. Current state of text sentiment analysis from opinion to emotion mining
24. Imitation learning: A survey of learning methods
25. Kaur, H., and Mangat, V. (2017, January 10–11). A survey of sentiment analysis techniques. Proceedings of the 2017 International Conference on the I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India.10.1109/I-SMAC.2017.8058315 ↗
26. Ontology-based sentiment analysis process for social media content10.1016/j.procs.2015.07.295 ↗
27. Ontology-based sentiment analysis of twitter posts10.1016/j.eswa.2013.01.001 ↗
28. Sentiment analysis of Chinese microblogging based on sentiment ontology: A case study of ‘7.23 Wenzhou Train Collision10.1080/09540091.2013.851172 ↗
29. Opinion mining based on fuzzy domain ontology and support vector machine: A proposal to automate online review classification10.1016/j.asoc.2016.06.003 ↗
30. Feature-based opinion mining through ontologies10.1016/j.eswa.2014.03.022 ↗
31. An ontology-based text-mining method to cluster proposals for research project selection10.1109/TSMCA.2011.2172205 ↗
32. An ontology-based web mining method for unemployment rate prediction10.1016/j.dss.2014.06.007 ↗
33. Ontology-based sentiment analysis of network public opinions
34. Review-aggregated aspect-based sentiment analysis with ontology features10.1007/s13748-018-0163-7 ↗
35. Le Thi, T., Thanh, T.Q., and Thi, T.P. (2017, January 7–8). Ontology-Based Entity Coreference Resolution for Sentiment Analysis. Proceedings of the Eighth Symposium on Information and Communication Technology (SoICT 2017), Nha Trang, Vietnam.10.1145/3155133.3155168 ↗
36. A model for sentiment analysis based on ontology and cases10.1109/TLA.2016.7795829 ↗
37. Shojaee, S., Murad, M.A.A., Azman, A.B., Sharef, N.M., and Nadali, S. (2013, January 8–10). Detecting deceptive reviews using lexical and syntactic features. Proceedings of the 13th International Conference on Intelligent Systems Design and Applications (ISDA), Bangi, Malaysia.10.1109/ISDA.2013.6920707 ↗
38. TOM: Twitter opinion mining framework using hybrid classification scheme10.1016/j.dss.2013.09.004 ↗
39. Detecting sentiment embedded in Arabic social media—A lexicon-based approach10.3233/IFS-151574 ↗
40. Asghar, M.Z., Khan, A., Ahmad, S., Qasim, M., and Khan, I.A. (2017). Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PLoS ONE, 12.10.1371/journal.pone.0171649 ↗
41. Thakkar. Senti-N-Gram: An n-gram lexicon for sentiment analysis10.1016/j.eswa.2018.03.004 ↗
42. Adapting sentiment lexicons to domain-specific social media texts10.1016/j.dss.2016.11.001 ↗
43. Sentiment analysis of player chat messaging in the video game StarCraft 2: Extending a lexicon-based model10.1016/j.knosys.2017.09.022 ↗
44. Asghar, M.Z., Khan, A., Ahmad, S., Khan, I.A., and Kundi, F.M. (2015). A unified framework for creating domain dependent polarity lexicons from user generated reviews. PLoS ONE, 10.10.1371/journal.pone.0140204 ↗
45. Senti-lexicon and improved NB algorithms for sentiment analysis of restaurant reviews10.1016/j.eswa.2011.11.107 ↗
46. Zeng, L., and Li, F. (2013). A classification-based approach for implicit feature identification. Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, Springer.10.1007/978-3-642-41491-6_18 ↗
47. Bidirectional LSTM Malicious webpages detection algorithm based on convolutional neural network and independent recurrent neural network10.1007/s10489-019-01433-4 ↗
48. Feature-based opinion mining and ranking10.1016/j.jcss.2011.10.007 ↗
49. Sentiment Analysis in monitoring software development processes: An exploratory case study on GitHub’s project issues10.1016/j.jss.2015.02.055 ↗
50. Cheng, K., Li, J., Tang, J., and Liu, H. (2017, January 4–9). Unsupervised sentiment analysis with signed social networks. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.10.1609/aaai.v31i1.11008 ↗
51. Pujari, C., and Shetty, N.P. (2018). Comparison of Classification Techniques for Feature Oriented Sentiment Analysis of Product Review Data. Data Engineering and Intelligent Computing, Springer.10.1007/978-981-10-3223-3_14 ↗
52. Fusing audio, visual and textual clues for sentiment analysis from multimodal content10.1016/j.neucom.2015.01.095 ↗
53. Valstar, M., Gratch, J., Schuller, B., Ringeval, F., Lalanne, D., Torres Torres, M., Scherer, S., Stratou, G., Cowie, R., and Pantic, M. Avec 2016: Depression, mood, and emotion recognition workshop and challenge. Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge.10.1145/2988257.2988258 ↗
54. Moh, M., Gajjala, A., Gangireddy, S.C.R., and Moh, T.S. (2015, January 6–9). On multi-tier sentiment analysis using supervised machine learning. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Singapore.10.1109/WI-IAT.2015.154 ↗
55. Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., and Manning, C.D. (2011, January 27–31). Semi-supervised recursive autoencoders for predicting sentiment distributions. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK.
56. Davidov, D., Tsur, O., and Rappoport, A. (2010, January 15–16). Semi-supervised recognition of sarcastic sentences in twitter and amazon. Proceedings of the Fourteenth Conference on Computational Natural Language Learning, Uppsala, Sweden.
57. Liu, S., Li, F., Li, F., Cheng, X., and Shen, H. (November, January 27). Adaptive co-training SVM for sentiment classification on tweets. Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, San Francisco, CA, USA.
58. Forecasting Stock Market Movement Direction Using Sentiment Analysis and Support Vector Machine10.1109/JSYST.2018.2794462 ↗
59. Gamallo, P., and Garcia, M. (2014, January 23–24). A Naive-Bayes Strategy for Sentiment Analysis on English Tweets. Proceedings of the 8th International Workshop on Semantic Evaluation SemEval, Dulbin, Ireland.10.3115/v1/S14-2026 ↗
60. Unsupervised representation learning based on the deep multi-view ensemble learning
61. A quantum-inspired sentiment representation model for twitter sentiment analysis10.1007/s10489-019-01441-4 ↗
62. A machine learning approach to sentiment analysis in multilingual Web texts10.1007/s10791-008-9070-z ↗
63. Akaichi, J. (2014, January 29–30). Sentiment Classification at the Time of the Tunisian Uprising: Machine Learning Techniques Applied to a New Corpus for Arabic Language. Proceedings of the 2014 European Network Intelligence Conference (ENIC), Wroclaw, Poland.10.1109/ENIC.2014.35 ↗
64. Povoda, L., Burget, R., and Dutta, M.K. (2016, January 27–29). Sentiment analysis based on Support Vector Machine and Big Data. Proceedings of the 2016 39th International Conference on Telecommunications and Signal Processing (TSP), Vienna, Austria.10.1109/TSP.2016.7760939 ↗
65. Classification of sentimental reviews using machine learning techniques10.1016/j.procs.2015.07.523 ↗
66. Ensemble of feature sets and classification algorithms for sentiment classification10.1016/j.ins.2010.11.023 ↗
67. Retrieving product features and opinions from customer reviews10.1109/MIS.2013.37 ↗
68. Recurrent convolutional neural networks for text classification
69. Duwairi, R.M. (2015, January 7–9). Sentiment analysis for dialectical Arabic. Proceedings of the 2015 6th International Conference on Information and Communication Systems (ICICS), Amman, Jordan.10.1109/IACS.2015.7103221 ↗
70. Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN10.1016/j.eswa.2016.10.065 ↗
71. Wang, J., Yu, L.C., Lai, K.R., and Zhang, X. (2016, January 7–12). Dimensional sentiment analysis using a regional CNN-LSTM model. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics 2, Berlin, Germany.10.18653/v1/P16-2037 ↗
72. Guggilla, C., Miller, T., and Gurevych, I. (2016, January 11–16). CNN-and LSTM-based claim classification in online user comments. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan.
73. A study of the effects of preprocessing strategies on sentiment analysis for Arabic text10.1177/0165551514534143 ↗
74. Time corpora: Epochs, opinions and changes10.1016/j.knosys.2014.04.029 ↗
75. Neviarouskaya, A., Prendinger, H., and Ishizuka, M. (2009, January 17–20). Compositionality Principle in Recognition of Fine-Grained Emotions from Text. Proceedings of the ICWSM, San Jose, CA, USA.10.1609/icwsm.v3i1.13987 ↗
76. Fei, G., Liu, B., Hsu, M., Castellanos, M., and Ghosh, R. (2012, January 8–15). A dictionary-based approach to identifying aspects implied by adjectives for opinion mining. Proceedings of the COLING 2012: Posters, Mumbai, India.
77. Cui, H., Mittal, V., and Datar, M. (2006, January 16–20). Comparative experiments on sentiment classification for online product reviews. Proceedings of the AAAI’06 Proceedings of the 21st National Conference on Artificial Intelligence, Boston, MA, USA.
78. The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices10.1016/j.eswa.2016.12.036 ↗
79. Comparative study of classification algorithms used in sentiment analysis
80. Pang, B., and Lee, L. (2004, January 21–26). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Barcelona, Spain.10.3115/1218955.1218990 ↗
81. Priyanka, D.Y., and Senthilkumar, R. (2016, January 8–9). Sampling techniques for streaming dataset using sentiment analysis. Proceedings of the 2016 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, India.10.1109/ICRTIT.2016.7569580 ↗
82. Comparing a traditional approach for financial Brand Communication Analysis with a Big Data Analytics technique10.1109/ACCESS.2019.2905301 ↗
83. Onyx: A linked data approach to emotion representation10.1016/j.ipm.2015.03.007 ↗
84. Cambria, E., Poria, S., Bajpai, R., and Schuller, B. (2016, January 11–16). SenticNet 4: A semantic resource for sentiment analysis based on conceptual primitives. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan.
85. Sentiment analysis system adaptation for multilingual processing: The case of tweets10.1016/j.ipm.2014.10.004 ↗
86. Multi-lingual opinion mining on youtube10.1016/j.ipm.2015.03.002 ↗
87. Socher, R., Huval, B., Manning, C.D., and Ng, A.Y. (2012, January 12–14). Semantic compositionality through recursive matrix-vector spaces. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea.
88. Wang, X., Jiang, W., and Luo, Z. (2016, January 11–16). Combination of convolutional and recurrent neural network for sentiment analysis of short texts. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan.
89. Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis10.1016/j.dss.2014.05.005 ↗
90. Affective computing and sentiment analysis10.1109/MIS.2016.31 ↗
91. New avenues in opinion mining and sentiment analysis10.1109/MIS.2013.30 ↗
92. Conroy, N.J., Rubin, V.L., and Chen, Y. (2015, January 6–10). Automatic deception detection: Methods for finding fake news. Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community, St. Louis, MO, USA.
93. The future of host cell protein (HCP) identification during process development and manufacturing linked to a risk-based management for their control10.1002/bit.25628 ↗
94. Sentiment analysis on social media for stock movement prediction10.1016/j.eswa.2015.07.052 ↗
95. A sentiment analysis of US local government tweets: The connection between tone and citizen involvement10.1016/j.giq.2015.03.003 ↗
96. Van den Broek-Altenburg, E.M., and Atherly, A.J. (2019). Using Social Media to Identify Consumers’ Sentiments towards Attributes of Health Insurance during Enrollment Season. Appl. Sci., 9.10.3390/app9102035 ↗
97. Bologna, G., and Hayashi, Y. (2018). A rule extraction study from svm on sentiment analysis. Big Data Cogn. Comput., 2.10.3390/bdcc2010006 ↗
98. Understanding# WorldEnvironmentDay user opinions in Twitter: A topic-based sentiment analysis approach10.3390/ijerph15112537 ↗
99. Ren, G., and Hong, T. (2017). Investigating online destination images using a topic-based sentiment analysis approach. Sustainability, 9.10.3390/su9101765 ↗
100. Wiemer, H., Drowatzky, L., and Ihlenfeldt, S. (2019). Data Mining Methodology for Engineering Applications (DMME)—A Holistic Extension to the CRISP-DM Model. Appl. Sci., 9.10.3390/app9122407 ↗