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@article{190637,
        author = {Srinivas Pasupuleti and Chintalapati Hariharan and MDN Akash and S Venu Gopal},
        title = {A Comprehensive Review of the Evolution of Natural Language Processing Techniques for Sentiment Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {1420-1425},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190637},
        abstract = {Sentiment analysis has become a cornerstone task in natural language processing (NLP), fueled by the exponen- tial growth of user-generated content and its immense value for business, political, and social applications. Over the last two decades, the field has progressed through several dis- tinct paradigms, evolving from rule-based lexicon systems to feature-engineered machine learning models, and subsequently to representation-learning-based deep learning and large-scale transformer architectures. This paper provides an in-depth, comprehensive review of this technical evolution. We present a detailed analysis of the seminal contributions and methodologies within each paradigm, offering a structured comparison of their advantages, disadvantages, and underlying assumptions. We survey key benchmark datasets that have driven progress and the evaluation metrics used to measure it. Furthermore, we conduct a thorough examination of persistent challenges, including sarcasm, domain adaptation, multilingual analysis, and critical ethical considerations. Finally, we synthesize these findings to propose promising future research directions, aiming to provide a valuable roadmap for both new and experienced researchers and practitioners in the field of sentiment analysis.},
        keywords = {Natural Language Processing, Sentiment Analy- sis, Opinion Mining, Machine Learning, Deep Learning, Trans- formers, Literature Review.},
        month = {January},
        }