Journal of Intelligent Communication

Review

From Lexicons to Transformers: An AI View of Sentiment Analysis

Downloads

https://doi.org/10.54963/jic.v4i2.1434
[1]
Leon, M. 2025. From Lexicons to Transformers: An AI View of Sentiment Analysis. Journal of Intelligent Communication. 4, 2 (Sep. 2025), 13–25. DOI:https://doi.org/10.54963/jic.v4i2.1434.
Volume 4 Issue 2: September 2025
Copyright (c) 2025 Maikel Leon
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright

The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.

License

Journal of Intelligent Communication (JIC)  publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by Journal of Intelligent Communication (JIC) agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source is properly cited, anyone may copy, redistribute, reuse and transform the content.

Authors

  • Maikel Leon

    Department of Business Technology, Miami Herbert Business School, University of Miami, Coral Gables, FL 33146, USA

Received: 2 July 2025; Revised: 13 August 2025; Accepted: 17 August 2025; Published: 30 August 2025

Understanding public opinion at scale is both a scientific challenge and a practical necessity in the digital era, as the proliferation of online communication platforms has created unprecedented opportunities to monitor attitudes in near real time. Early work in subjectivity detection and semantic orientation laid the methodological foundations for automated sentiment extraction, focusing on distinguishing objective from subjective content and determining polarity. Contemporary applications, however, face far more complex requirements, demanding systems capable of processing massive, noisy, and dynamic data streams while integrating multimodal signals from text, images, audio, and video. This paper presents a historical review of sentiment analysis and opinion monitoring through the lens of artificial intelligence, tracing developments from the early 1990s to the present and classifying approaches from lexicon‑based heuristics to classical machine learning, deep neural architectures, transfer learning, and multimodal fusion, with an emphasis on both technical and conceptual advances. Extensive tables summarize algorithms, datasets, and case studies across various domains, including politics, finance, and entertainment, highlighting practical lessons and performance trends. The review also addresses pressing ethical concerns, including bias, fairness, and transparency, and considers the implications of rapidly evolving AI capabilities. We conclude by outlining future directions that emphasize adaptability, context awareness, and the seamless integration of emerging technologies into scalable and reliable opinion analysis systems.

Keywords:

Sentiment Analysis Public Opinion Monitoring Lexicon‑Based Techniques Deep Learning Multimodal Sentiment Integration

References

  1. Nasukawa, T.; Yi, J. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture, Sanibel Island, FL, USA, 23–25 October 2003; pp. 70–77. DOI: https://doi.org/10.1145/945645.945658
  2. Dave, K.; Lawrence, S.; Pennock, D.M. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on World Wide Web, Budapest, Hungary, 20–24 May 2003; pp. 519–528. DOI: https://doi.org/10.1145/775152.775226
  3. Turney, P.D. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, PA, USA, 6–12 July 2002; pp. 417–424.
  4. Pang, B.; Lee, L.; Vaithyanathan, S. Thumbs up? sentiment classification using machine learning techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Philadelphia, PA, USA, 6–7 July 2002; pp. 79–86. DOI: https://doi.org/10.3115/1118693.1118704
  5. Baccianella, S.; Esuli, A.; Sebastiani, F. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), Valletta, Malta, 17–23 May 2010; pp. 2200–2204.
  6. Pang, B.; Lee, L. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), Ann Arbor, MI, USA, 25–30 June 2005; pp. 115–124. DOI: https://doi.org/10.3115/1219840.1219855
  7. Wilson, T.; Wiebe, J.; Hoffmann, P. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, BC, Canada, 6–8 October 2005; pp. 347–354. DOI: https://doi.org/10.3115/1220575.1220619
  8. Devlin, J.; Chang, M.-W.; Lee, K.; et al. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv 2018, arXiv:1810.04805.
  9. Radford, A.; Narasimhan, K.; Salimans, T.; et al. Improving language understanding by generative pre-training. OpenAI Tech. Rep. 2018. Available from: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
  10. Poria, S.; Cambria, E.; Bajpai, R.; et al. Deep convolutional neural network text representations and multimodal fusion for emotion recognition. In Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition (FG), Ljubljana, Slovenia, 4–8 May 2015; pp. 873–878.
  11. Tian, Y.; Tian, Y.; Xia, F.; Song, Y. Learning multimodal contrast with cross-modal memory and reinforced contrast recognition. In Findings of the Association for Computational Linguistics ACL 2024; Association for Computational Linguistics: Bangkok, Thailand, 2024; pp. 6561–6573.
  12. Asur, S.; Huberman, B.A. Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Toronto, ON, Canada, 31 August–3 September 2010; pp. 492–499. DOI: https://doi.org/10.1109/WI-IAT.2010.63
  13. Bollen, J.; Mao, H.; Zeng, X.-J. Twitter mood predicts the stock market. J. Comput. Sci. 2011, 2, 1–8. DOI: https://doi.org/10.1016/j.jocs.2010.12.007
  14. O’Connor, B.; Balasubramanyan, R.; Routledge, B.; et al. From tweets to polls: Linking text sentiment to public opinion time series. Proc. Int. AAAI Conf. Web Soc. Media 2010, 4, 122–129. DOI: https://doi.org/10.1609/icwsm.v4i1.14031
  15. Tumasjan, A.; Sprenger, T.O.; Sandner, P.; et al. Predicting elections with twitter: What 140 characters reveal about political sentiment. Proc. Int. AAAI Conf. Web Soc. Media 2010, 4, 178–185. DOI: https://doi.org/10.1609/icwsm.v4i1.14009
  16. Hatzivassiloglou, V.; McKeown, K.R. Predicting the semantic orientation of adjectives. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, Madrid, Spain, 7–12 July 1997; Association for Computational Linguistics: Madrid, Spain, 1997; pp. 174–181. DOI: https://doi.org/10.3115/976909.979640
  17. Mikolov, T.; Chen, K.; Corrado, G.; et al. Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR), Scottsdale, AZ, USA, 2–4 May 2013.
  18. Pennington, J.; Socher, R.; Manning, C.D. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25–29 October 2014; pp. 1532–1543.
  19. Vaswani, A.; Shazeer, N.; Parmar, N.; et al. Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4–9 December 2017; pp. 5998–6008.
  20. Wiebe, J.M.; Bruce, R.F.; O’Hara, T.P. Development and use of a goldstandard data set for subjectivity classifications. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, College Park, MD, USA, 20–26 June 1999; pp. 246–253.
  21. Hu, M.; Liu, B. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Seattle, WA, USA, 22–25 August 2004; pp. 168–177. DOI: https://doi.org/10.1145/1014052.1014073
  22. Kim, S.-M.; Hovy, E. Determining the sentiment of opinions. In COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics; COLING: Geneva, Switzerland, 2004; pp. 1367–1373.
  23. Cambria, E., Hussain, A. Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis; Springer: Cham, Switzerland, 2015. DOI: https://dl.acm.org/doi/10.5555/2878632
  24. Riloff, E.; Wiebe, J. Learning subjective nouns using extraction pattern bootstrapping. In Proceedings of the Seventh Conference on Natural Language Learning (CoNLL), Edmonton, AB, Canada, 31 May–1 June: 2003; pp. 25–32.
  25. Nápoles, G.; Hoitsma, F.; Knoben, A.; et al. Prolog-based agnostic explanation module for structured pattern classification. Inf. Sci. 2023, 622, 1196–1227.
  26. Boiy, E.; Moens, M.-F. Boiy, E.; Moens, M.F. A machine learning approach to sentiment analysis in multilingual web texts. Inform. Retrieval 2009, 12, 526–558.
  27. Blitzer, J.; Dredze, M.; Pereira, F. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, Prague, Czech Republic, 25–27 June 2007; pp. 440–447.
  28. Mohammad, S.M.; Turney, P.D. Crowdsourcing a word–emotion association lexicon. Comput. Intell. 2013, 29, 436–465. DOI: https://doi.org/10.1111/j.1467-8640.2012.00460.x
  29. Poria, S.; Cambria, E.; Winterstein, G.; et al. Sentic patterns: Dependency-based rules for concept-level sentiment analysis. Knowl.-Based Syst. 2014, 69, 45–63. DOI: https://doi.org/10.1016/j.knosys.2014.05.005
  30. Leon, M.; Mkrtchyan, L.; Depaire, B.; et al. Learning and clustering of fuzzy cognitive maps for travel behaviour analysis. Knowl. Inf. Syst. 2014, 39, 435–462.
  31. DeSimone, H.; Leon, M. Explainable ai: The quest for transparency in business and beyond. In Proceedings of the 2024 7th IEEE International Conference on Information and Computer Technologies (ICICT), Honolulu, HI, USA, 15–17 March 2024; pp. 532–538.
  32. Socher, R.; Perelygin, A.; Wu, J.; et al. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, WA, USA, 18–21 October 2013; pp. 1631–1642.
  33. Kim, Y. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25–29 October 2014; pp. 1746–1751.
  34. Bahdanau, D.; Cho, K.; Bengio, Y. Neural machine translation by jointly learning to align and translate. In Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA, 7–9 May 2015.
  35. Young, T.; Hazarika, D.; Poria, S.; et al. Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 2018, 13, 55–75.
  36. Qiu, X.; Sun, T.; Xu, Y.; et al. Pre-trained models for natural language processing: A survey. Sci. China Technol. Sci. 2020, 63, 1872–1897. DOI: https://doi.org/10.1007/s11431-020-1647-3
  37. Tang, D.; Qin, B.; Liu, T. Aspect level sentiment classification with deep memory network. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA, 1–4 November 2016; pp. 214–224.
  38. Leon, M.; Depaire, B.; Vanhoof, K. Fuzzy cognitive maps with rough concepts. In Artificial Intelligence Applications and Innovations: 9th IFIP WG 12.5 International Conference, AIAI 2013, Proceedings; Springer Berlin Heidelberg: Berlin/Heidelberg, Germany, 2013; pp. 527–536.
  39. Daumé, H., III. Frustratingly easy domain adaptation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, Prague, Czech Republic, 25–27 June 2007; pp. 256–263.
  40. Abdalla, M.; Hirst, G. 2017. Cross-Lingual Sentiment Analysis Without (Good) Translation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing, Taipei, Taiwan, 27 November–1 December 2017.
  41. Abdellatif, A.; Sahmoud, S.; Nizam, A. A Unified Framework for Multi-Language Sentiment Analysis. In Proceedings of the 2023 3rd International Conference on Computing and Information Technology (ICCIT), Tabuk, Saudi Arabia, 13–14 September 2023.
  42. Ganin, Y.; Lempitsky, V. Domain-adversarial training of neural networks. In Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 20–22 June 2016; pp. 209–219.
  43. Ho, T.T.; Huang, Y. Stock price movement prediction using sentiment analysis and CandleStick chart representation. Sensors 2021, 21, 7957.
  44. Felbo, B.; Mislove, A.; Søgaard, A.; et al. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, Denmark, 7–11 September 2017; pp. 1616–1626.
  45. Leon, M.; Martinez Sanchez, N.; Garcia Valdivia, Z.; et al. Concept maps combined with case-based reasoning in order to elaborate intelligent teaching/learning systems. In Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), Rio de Janeiro, Brazil, 20–24 October 2007; pp. 205–210.
  46. Ceron, A.; Curini, L.; Iacus, S.M.; et al. Every tweet counts? how sentiment analysis of social media can improve our knowledge of citizens’ political preferences. New Media Soc. 2014, 16, 340–358. DOI: https://doi.org/10.1177/1461444813480466
  47. Pak, A.; Paroubek, P. Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC 2010), Valletta, Malta, 17–23 May 2010; pp. 1320–1326.
  48. Kouloumpis, E.; Wilson, T.; Moore, J.D. Twitter sentiment analysis: The good the bad and the omg! Proc. Int. AAAI Conf. Web Soc. Media 2011, 5, 538–541.
  49. Go, A.; Bhayani, R.; Huang, L. Twitter sentiment classification using distant supervision. 2009. Available from: https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf
  50. Maqbool, J.; Aggarwal, P.; Kaur, R.; et al. Stock prediction by integrating sentiment scores of financial news and MLP-regressor: A machine learning approach. Procedia Comput. Sci. 2023, 218, 1067–1078.
  51. Leon, M. The escalating ai’s energy demands and the imperative need for sustainable solutions. WSEAS Trans. Syst. 2024, 23, 444–457.
  52. Leon, M.; Nápoles, G.; Garcı́a, M.M.; et al. Two steps individuals travel behavior modeling through fuzzy cognitive maps pre-definition and learning. In Advances in Soft Computing: 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings, Part II; Springer Berlin Heidelberg: Berlin/Heidelberg, Germany, 2011; pp. 82–94.
  53. Leon, M. Aggregating procedure for fuzzy cognitive maps. Int. FLAIRS Conf. Proc. 2023, 36. DOI: https://doi.org/10.32473/flairs.36.133082