JACIII Vol.27 No.1 pp. 84-95
doi: 10.20965/jaciii.2023.p0084


Beyond Sentiment Analysis: A Review of Recent Trends in Text Based Sentiment Analysis and Emotion Detection

Lai Po Hung and Suraya Alias

Universiti Malaysia Sabah
Jalan UMS, Kota Kinabalu, Sabah 88400, Malaysia

Corresponding author

September 17, 2021
August 23, 2022
January 20, 2023
sentiment analysis, emotion detection, text, machine learning, deep learning

Sentiment Analysis is probably one of the best-known area in text mining. However, in recent years, as big data rose in popularity more areas of text classification are being explored. Perhaps the next task to catch on is emotion detection, the task of identifying emotions. This is because emotions are the finer grained information which could be extracted from opinions. So besides writer sentiments, writer emotion is also a valuable data. Emotion detection can be done using text, facial expressions, verbal communications and brain waves; however, the focus of this review is on text-based sentiment analysis and emotion detection. The internet has provided an avenue for the public to express their opinions easily. These expressions not only contain positive or negative sentiments, it contains emotions as well. These emotions can help in social behaviour analysis, decision and policy makings for companies and the country. Emotion detection can further support other tasks such as opinion mining and early depression detection. This review provides a comprehensive analysis of the shift in recent trends from text sentiment analysis to emotion detection and the challenges in these tasks. We summarize some of the recent works in the last five years and look at the methods they used. We also look at the models of emotion classes that are generally referenced. The trend of text-based emotion detection has shifted from the early keyword-based comparisons to machine learning and deep learning algorithms that provide more flexibility to the task and better performance.

Cite this article as:
L. Hung and S. Alias, “Beyond Sentiment Analysis: A Review of Recent Trends in Text Based Sentiment Analysis and Emotion Detection,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.1, pp. 84-95, 2023.
Data files:
  1. [1] S. Shayaa et al., “Sentiment Analysis of Big Data: Methods, Applications, and Open Challenges,” IEEE Access, Vol.6, pp. 37807-37827, 2018.
  2. [2] K. Sailunaz and R. Alhajj, “Emotion and Sentiment Analysis from Twitter Text,” J. of Computational Science, Vol.36, Article No.101003, 2019.
  3. [3] F. Song, S. Liu, and J. Yang, “A comparative study on text representation schemes in text categorization,” Pattern Analysis and Application, Vol.8, pp. 199-209, 2005.
  4. [4] B. S. Harish, D. S. Guru, and S. Manjunath, “Representation and Classification of Text Documents: A Brief Review,” IJCA Special Issue on Recent Trends in Image Processing and Pattern Recognition, No.2, pp. 110-119, 2010.
  5. [5] L. P. Hung, R. Alfred, M. H. Ahmad Hijazi, and A. A. Ag. Ibrahim, “A Review on the Ensemble Framework for Sentiment Analysis,” Advanced Science Letters, Vol.21, No.10, pp. 2957-2962, 2015.
  6. [6] O. Bruna, H. Avetisyan, and J. Holub, “Emotion models for textual emotion classification,” J. of Physics: Conf. Series, Vol.772, Article No.012063, 2016.
  7. [7] K. Oatley, D. Keltner, and J. M. Jenkins, “Understanding Emotions,” Blackwell Publishing, 2006.
  8. [8] S. Lee, “A Linguistic Analysis of Implicit Emotions,” Workshop on Chinese Lexical Semantics, pp. 185-194, 2015.
  9. [9] T. Baldwin et al., “How Noisy Social Media Text, How Diffrnt Social Media Sources?,” Proc. of Int. Joint Conf. on Natural Language Processing, pp. 356-364, 2013.
  10. [10] P. Ingole, S. Bhoir, and A. V. Vidhate, “Hybrid Model for Text Classification,” Proc. of 2nd Int. Conf. on Electronics, Communication and Aerospace Technology, pp. 7-15, 2018.
  11. [11] J. K. Rout et al., “A Model for Sentiment and Emotion Analysis of Unstructured Social Media Text,” Electron. Commer. Res., Vol.18, pp. 181-199, 2018.
  12. [12] H. Saif, F. J. Ortega, M. Fernandez, and I. Cantador, “Sentiment Analysis in Social Streams,” Chapter in Emotions and Personality in Personalized Services, 2016.
  13. [13] F. Kateb and J. Kalita, “Classifying Short Text in Social Media: Twitter as Case Study,” Int. J. of Computer Applications, Vol.111, No.9, pp. 1-12, 2015.
  14. [14] E. Kauffmann, J. Peral, D. Gil, A. Ferrández, R. Sellers, and H. Mora, “A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making,” Industrial Marketing Management, Vol.90, pp. 523-537, 2020.
  15. [15] L. Holla and K. Kavitha, “A comparative study on fake review detection techniques,” Int. J. of Engineering Research in Computer Science and Engineering (IJERCSE), Vol.5, No.4, 2018.
  16. [16] T. Hai, K. Shirai, and J. Velcin, “Sentiment analysis on social media for stock movement prediction,” Expert Syst. Appl., Vol.42, No.24, pp. 9603-9611, 2015.
  17. [17] R. Ren and D. D. Wu, “Forecasting stock market movement direction using sentiment analysis and support vector machine,” IEEE Syst. J., Vol.13, No.1, pp. 760-770, 2019.
  18. [18] S. Sohangir, D. Wang, A. Pomeranets, and T. M. Khoshgoftaar, “Big Data: Deep Learning for Financial Sentiment Analysis,” J. of Big Data, Vol.5, No.3, 2018.
  19. [19] E. Georgiadou, S. Angelopoulos, and H. Drake, “Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes,” Int. J. of Information Management, Vol.51, Article No.102048, 2020.
  20. [20] J. R. Ragini, P. M. R. Anand, and V. Bhaskar, “Big data analytics for disaster response and recovery through sentiment analysis,” Int. J. of Information Management, Vol.42, pp. 13-24, 2018.
  21. [21] P. Turney, “Thumbs Up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification of Reviews,” Proc. of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417-424, 2002.
  22. [22] A. M. Popescu and O. Etzioni, “Extracting Product Features and Opinions from Reviews,” Proc. of the Conf. on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 339-346, 2005.
  23. [23] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs Up? Sentiment Classification Using Machine Learning Techniques,” Proc. of the Conf. on Empirical Methods in Natural Language Processing (EMNLP), pp. 79-86, 2002.
  24. [24] O. Appel, F. Chiclana, J. Carter, and H. Fujita, “A hybrid approach to the sentiment analysis problem at the sentence level,” Knowledge-Based Systems, Vol.108, pp. 110-124, 2016.
  25. [25] F. Khan, U. Qamar, and S. Bashir, “eSAP: A decision support framework for enhanced sentiment analysis and polarity classification,” Information Sciences, Vol.367-368, pp. 862-873, 2016.
  26. [26] D. Tang et al., “Sentiment Embeddings with Applications to Sentiment Analysis,” IEEE Trans. on Knowledge and Data Engineering, Vol.28, No.2, pp. 496-509, 2016.
  27. [27] A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of sentiment reviews using N-gram machine learning approach,” Expert Systems with Applications, Vol.57, pp. 117-126, 2016.
  28. [28] S. Poria, I. Chaturvedi, E. Cambria, and A. Hussain, “Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis,” Proc. of 2016 IEEE 16th Int. Conf. on Data Mining, pp. 439-448, 2016.
  29. [29] A. Onan, S. Korukoglu, and H. Bulut, “A Multiobjective Weighted Voting Ensemble Based on Differential Evolution Algorithm for Text Sentiment Classification,” Expert Systems with Applications, Vol.62, pp. 1-16, 2016.
  30. [30] M. T. Al-Sharuee, F. Liu, and M. Pratama, “Sentiment Analysis: An Automatic Contextual Analysis and Ensemble Clustering Approach and Comparison,” Data & Knowledge Engineering, Vol.115, pp. 194-213, 2018.
  31. [31] S. Xiong, H. Lv, W. Zhao, and D. Ji, “Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings,” Neurocomputing, Vol.275, pp. 2459-2466, 2018.
  32. [32] N. S. Ankit, “An Ensemble Classification System for Twitter Sentiment Analysis,” Proc. of Int. Conf. on Computational Intelligence and Data Science (ICCIDS 2018), pp. 937-946, 2018.
  33. [33] J. Akilandeswari and G. Jothi, “Sentiment Classification of Tweets with Non-Language Features,” Procedia Computer Science, Vol.143, pp. 426-433, 2018.
  34. [34] A. S. Imran, S. M. Daudpota, Z. Kastrati, and R. Batra, “Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets,” IEEE Access, Vol.8, pp. 181074-181090, 2020.
  35. [35] E. Ohman, M. Pamies, K. Kajava, and J. Tiedemanm, “XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection,” Proc. of the 28th Int. Conf. on Computational Linguitics, 2020.
  36. [36] Scholarly Analysis, [Accessed August 24, 2021]
  37. [37] P. Ekman, “Basic Emotions,” Handb. Cogn. Emot., pp. 45-60, 1999.
  38. [38] R. Plutchik, “The Nature of Emotions,” Am. Sci., Vol.89, No.4, pp. 344-350, 2001.
  39. [39] W. G. Parrott, “Emotions in Social Psychology: Essential Readings,” Psychology Press, 2001.
  40. [40] P. Shaver et al., “Emotion knowledge: Further exploration of a prototype approach,” J. Pers. Soc. Psychol., Vol.52, No.6, Article No.1061, 1987.
  41. [41] A. Ortony, G. Clore, and A. Collins, “The Cognitive Structure of Emotions,” Cambridge University Press, 1988.
  42. [42] A. Yadollahi, A. G. Shahraki, and O. R. Zaiane, “Current State of Text Sentiment Analysis from Opinion to Emotion Mining,” ACM Computing Surveys, Vol.50, No.2, pp. 1-33, 2017.
  43. [43] B. Gaind, V. Syal, and S. Padgalwar, “Emotion Detection and Analysis on Social Media,” Proc. of the Int. Conf. on Recent Trends in Computational Engineering and Technologies, 2018.
  44. [44] I. Perikos and I. Hatzilygeroudis, “Recognizing emotions in text using ensemble of classifiers,” Engineering Applications of Artificial Intelligence, Vol.51, pp. 191-201, 2016.
  45. [45] N. Shelke, “Approaches of emotion detection from text,” Int. J. Comput. Sci. Inf. Technol. Res., Vol.2, No.2, pp. 123-128, 2014.
  46. [46] L. Canales and P. Martinez-Barco, “Emotion detection from text: A survey,” Proc. of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC), pp. 37-43, 2014.
  47. [47] H. Binali, C. Wu, and V. Potdar, “Computational Approaches for Emotion Detection in Text,” Proc. of the 4th IEEE Int. Conf. on Digital Ecosystems and Technologies (IEEE DEST 2010), pp. 172-177, 2010.
  48. [48] J. A. Russell, “A Circumplex Model of Affect,” J. Pers. Soc. Psychol., Vol.39, No.6, Article No.1161, 1980.
  49. [49] E. Kao et al., “Towards text-based emotion detection: a survey and possible improvements,” Int. Conf. on Information Management and Engineering (ICIME’09), pp. 70-74, 2009.
  50. [50] R. Hirat and N. Mittal, “A Survey On Emotion Detection Techniques Using Text in Blogspots,” Int. Bulletin of Mathematical Research, Vol.2, No.1, pp. 180-187, 2015.
  51. [51] C. H. Wu, Z. J. Chuang, and Y. C. Lin, “Emotion Recognition from Text Using Semantic Labels and Separable Mixture Models,” ACM Trans. on Asian Language Information Processing (TALIP), Vol.5, No.2, pp. 165-183, 2006.
  52. [52] M. Chunling, H. Prendinger, and M. Ishizuka, “Emotion Estimation and Reasoning Based on Affective Textual Interaction,” Affective Computing and Intelligent Interaction, Vol.3784, pp. 622-628, 2005.
  53. [53] J. Hancock, C. Landrigan, and C. Silver, “Expressing emotion in text-based communication,” Proc. of the SIGCHI Conf. on Human Factors in Computing Systems (CHI 2007), pp. 929-932, 2007.
  54. [54] H. Li, N. Pang, and S. Guo, “Research on Textual Emotion Recognition Incorporating Personality Factor,” Int. Conf. on Robotics and Biomimetics, pp. 2222-2227, 2007.
  55. [55] C. Strapparava and R. Mihalcea, “Learning to identify emotions in text,” ACM Symp. on Applied Computing (SAC’08), pp. 1556-1560, 2008.
  56. [56] A. Agrawal and A. An, “Unsupervised emotion detection from text using sematic and syntactic relations,” The 2012 IEEE/WIC/ACM Int. Joint Conf. on Web Intelligence and Intelligent Agent Technology, Vol.1, pp. 346-353, 2012.
  57. [57] C. Alm, D. Roth, and R. Sproat, “Emotions from Text: Machine Learning for Text-Based Emotion Prediction,” Proc. of Human Language Technology Conf. and Conf. on Empirical Methods in Natural Language Processing, pp. 579-586, 2005.
  58. [58] Z. Teng, F. Ren, and S. Kuroiwa, “Recognition of Emotion with SVMs,” D.-S. Huang, K. Li, and G. W. Irwin (Eds.), “Lecture Notes of Artificial Intelligence 4114,” pp. 701-710, Springer, 2006.
  59. [59] C. Yang, K. H. Y. Lin, and H. H. Chen, “Emotion classification using web blog corpora,” Proc. of the IEEE/WIC/ACM Int. Conf. on Web Intelligence. IEEE Computer Society, pp. 275-278, 2007.
  60. [60] Y. Wang and A. Pal, “Detecting Emotions in Social Media: A Constrained Optimization Approach,” Proc. of the 24th Int. Joint Conf. on Artificial Intelligence (IJCAI 2015), pp. 996-1002, 2015.
  61. [61] S. Shivhare, S. Garg, and A. Mishra, “EmotionFinder: Detecting Emotion from Blogs and Textual Documents,” Int. Conf. on Computing, Communication and Automation (ICCCA2015), pp. 52-57, 2015.
  62. [62] Y. Douili, M. Hajar, and H. A. Moatassime, “Using Youtube comments for text-based emotion recognition,” Procedia Comput Science, Vol.83, pp. 292-299, 2016.
  63. [63] J. Herzig, M. Shmueli-Scheuer, and D. Konopnicki, “Emotion Detection from Text via Ensemble Classification Using Word Embeddings,” Proc. of the ACM SIGIR Int. Conf. on Theory of Information Retrieval (ICTIR’17), pp. 269-272, 2017.
  64. [64] S. Mohammad and F. Bravo-Marquez, “Emotion Intensities in Tweets,” Proc. of the 6th Joint Conf. on Lexical and Computational Smeantics (*SEM 2017), pp. 65-77, 2017.
  65. [65] S. X. Mashal and K. Asnani, “Emotion Intensity Detection for Social Media Data,” Proc. of the IEEE 2017 Int. Conf. on Computing Methodologies and Communication, pp. 155-158, 2017.
  66. [66] R. V. Kumar, S. Rahmanian, and H. AlBalooshi, “EmotionX-SmartDubai_NLP: Detecting User Emotions In Social Media Text,” Proc. of the 6th Int. Workshop on Natural Language Processing for Social Media, pp. 45-49, 2018.
  67. [67] W. Witon, P. Colombo, A. Modi, and M. Kapadia, “Disney at IEST 2018: Predicting Emotions Using an Ensemble,” Proc. of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Assoc. for Computational Linguistics, pp. 248-253, 2018.
  68. [68] B. Kratzwaldet al., “Deep learning for affective computing: Text-based emotion recognition in Decision Support,” Decision Support Systems, Vol.115, pp. 24-35, 2018.
  69. [69] S. S. Ibraheim, S. S. Ismail, K. A. Bahnasy, and M. M. Aref, “Convolutional Neural Network Multi-Emotion Classifiers,” Jordanian J. of Computers and Information Technology (JJCIT), Vol.5, No.2, pp. 79-108, 2019.
  70. [70] E. Batbaatar, M. Li, and K. Ryu, “Semantic-Emotion Neural Network for Emotion Recognition from Text,” IEEE Access, Vol.7, pp. 111866-111878, 2019.
  71. [71] M. Baali and N. Ghneim, “Emotion Analysis of Arabic Tweets Using Deep Learning Approach,” J. of Big Data, Vol.6, No.89, 2019.
  72. [72] K. S. N. Tan, T. M. Lim, and Y. M. Lim, “Emotion Analysis Using Self-Training on Malaysian Code-Mixed Twitter Data,” Proc. of Int. Conf. ICT Society and Human Beings, pp. 181-188, 2020.
  73. [73] Z. Erenel, O. Adegboye, and H. Kusetogullari, “A New Feature Selection Scheme for Emotion Recognition from Text,” Applied Sciences, Vol.10, No.15, 2020.
  74. [74] S. Zad and M. A. Finlayson, “Systematic Evaluation of a Framework for Unsupervised Emotion Recognition for Narrative Text,” Proc. of the 1st Joint Workshop on Narrative Understanding, Storylines, and Events, pp. 26-37, 2020.
  75. [75] C. S. A. Razak, S. H. A. Hamid, H. Meon, H. A. Subramaniam, and N. B. Anuar, “Two-Step Model for Emotion Detection on Twitter Users: A COVID-19 Case Study in Malaysia,” Malaysian J. of Computer Science, Vol.34, No.4, pp. 374-388, 2021.
  76. [76] A. Chowanda, R. Sutoyo, M. Meiliana, and S. Tanachutiwat, “Exploring Text-Based Emotions Recognition Machine Learning Techniques on Social Media Conversation,” Procedia Computer Science, Vol.179, pp. 821-828, 2021.
  77. [77] R. Xia, C. Zong, and S. Li, “Ensemble of feature sets and classification algorithms for sentiment classification,” Information Sciences, Vol.181, No.6, pp. 1138-1152, 2011.
  78. [78] P. Lai and R. Alfred, “A Performance Comparison of Feature Extraction Methods for Sentiment Analysis,” Advanced Topics in Intelligent Information and Database Systems, Studies in Computational Intelligence, Vol.710, pp. 379-390, 2017.
  79. [79] P. H. Lai, J. Y. Chan, and K. O. Chin, “Ensembles for Text-Based Sarcasm Detection,” Proc. of 19th IEEE Student Conf. on Research and Development, pp. 284-289, 2021.

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