Sentiment Analysis: A Comprehensive Review of NLP Techniques on Social Media Data

Year : 2026 | Volume : 04 | Issue : 02 | Page : 35 42
By

Ritish Kumar Sharma,

Ankit Kumar,

Simran Kumari,

Tuba Fatma,

Pavitr Jain,

Sujeet Kumar,

  1. Student, Department of Computer Science and Engineering, Greater Noida Institute of Technology, Uttar Pradesh, India
  2. Student, Department of Computer Science and Engineering, Greater Noida Institute of Technology, Uttar Pradesh, India
  3. Student, Department of Computer Science and Engineering, Greater Noida Institute of Technology, Uttar Pradesh, India
  4. Student, Department of Computer Science and Engineering, Greater Noida Institute of Technology, Uttar Pradesh, India
  5. Student, Department of Computer Science and Engineering, Greater Noida Institute of Technology, Uttar Pradesh, India
  6. Faculty/Academician, Department of Computer Science and Engineering, Greater Noida Institute of Technology, Uttar Pradesh, India

Abstract

The growth of social media platforms has created a large volume of written content generated by users via interactions. The data consists of individuals’ opinions and feelings expressed about products and services and political and social issues. Manual methods have made it very difficult to process the massive amount of available data. Therefore, users are implementing sentiment analysis—a natural language processing (NLP) tool that allows individuals to understand communications from the public on the internet via analysis. This paper reviews prior research studies on how sentiment analysis uses various methods derived from NLP to analyze content found on social media. Some of these methods include word lists and definitions, machine learning methods, as well as various Deep Learning Techniques. The research paper discusses the various data preparation techniques and the data extraction process, as well as the types of datasets utilized by the author(s) in their research and the evaluation methods of their research. The research paper also assesses some of the challenges associated with using social media data for sentiment analysis (i.e., detecting sarcasm, multilingual data, and lack of data). That said, the research paper also provides an assessment of the strengths and weaknesses of each of the different sentiment analysis techniques being evaluated.

Keywords: Social media, natural language processing (NLP), sentiment analysis, opinion mining, machine learning

[This article belongs to International Journal of Algorithms Design and Analysis Review ]

How to cite this article: Ritish Kumar Sharma, Ankit Kumar, Simran Kumari, Tuba Fatma, Pavitr Jain, Sujeet Kumar. Sentiment Analysis: A Comprehensive Review of NLP Techniques on Social Media Data. International Journal of Algorithms Design and Analysis Review. 2026; 04(02):35-42.
How to cite this URL: Ritish Kumar Sharma, Ankit Kumar, Simran Kumari, Tuba Fatma, Pavitr Jain, Sujeet Kumar. Sentiment Analysis: A Comprehensive Review of NLP Techniques on Social Media Data. International Journal of Algorithms Design and Analysis Review. 2026; 04(02):35-42. Available from: https://journals.stmjournals.com/ijadar/article=2026/view=260495

References

  1. Kennedy A, Inkpen D. Sentiment classification of movie reviews using contextual valence shifters. Comput Intell. 2006;22(2):110-25. doi:10.1111/j.1467-8640.2006.00277.x.
  2. Mohammad SM, Turney PD. Crowdsourcing a word-emotion association lexicon. Comput Intell. 2013;29:436–465. doi:10.1111/j.1467-8640.2012.00460.x.
  3. Devlin J, Chang MW, Lee K, Toutanova K. 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. Vol. 1, Long and Short Papers; 2019. p.4171–4186. doi:10.18653/v1/N19-1423.
  4. Rosenthal S, Farra N, Nakov P. SemEval-2017 task 4: Sentiment analysis in Twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017); 2017. p.502–518. doi:10.18653/v1/S17-2088.
  5. 5Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, 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; 2013. p.1631–1642. doi:10.18653/v1/D13-1170.
  6. Liu B. Sentiment analysis. Cambridge: Cambridge University Press; 2020. Available from: https://www.cambridge.org/core/books/abs/sentiment-analysis/sentiment-analysis/0AB94D9F40A0402AB6EA566CB5776D30.
  7. Schouten K, Frasincar F. Survey on aspect-level sentiment analysis. IEEE Trans Knowl Data Eng. 2016;28:813–830. doi:10.1109/TKDE.2015.2485209.
  8. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv [Preprint]. 2013. Available from: https://arxiv.org/abs/1301.3781
  9. Bari P, Bedi G, Joshi K, Jawale A. Why transformers outperform LSTMs: A comparative study on sarcasm detection. J Artif Intell. 2025;7:499–508. doi:10.32604/jai.2025.072531.
  10. Yoshikawa T. Foundations of Robotics: Analysis and Control. Cambridge (MA): MIT Press; 1990. doi:10.7551/mitpress/3074.001.0001.
  11. Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov R, Le QV. XLNet: Generalized autoregressive pretraining for language understanding. arXiv [Preprint]. 2019. Available from: https://arxiv.org/abs/1906.08237
  12. Dediu AH, Matos M, Martín-Vide C. Natural language processing, moving from rules to data. In: International Conference on Theory and Applications of Models of Computation. Cham: Springer International Publishing; 2017. p.24–38.
Regular Issue Subscription Review Article
Volume 04
Issue 02
Received 24/06/2026
Accepted 04/08/2026
Published 10/09/2026
Publication Time 78 Days

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