APPLICATIONS, CHALLENGES, AND EMERGING TRENDS IN TWITTER SENTIMENT ANALYSIS – Volume 12 Issue 5

Twitter sentiment analysis has emerged as a critical area of research within Natural Language Processing (NLP) due to the widespread use of Twitter as a microblogging platform. This article reviews recent advances in sentiment analysis techniques, explores practical applications, discusses common challenges, and highlights emerging trends. The review integrates insights from prior studies, including the author’s previous contributions, to provide a comprehensive understanding of the current landscape and future research directions.

Twitter sentiment analysis has evolved into a sophisticated research domain with applications across marketing, politics, misinformation detection, and social behavior analysis. While deep learning techniques have improved accuracy, challenges such as informal language, sarcasm, and domain adaptation persist. Future research will likely focus on multimodal, multil

1.{Kavitha2021} P. Kavitha, “Twitter Sentiment Analysis Based on Adaptive Deep Recurrent Neural Network,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 9, pp. 2449–2457, 2021. 2.{Kavitha2018} P. Kavitha, “Twitter Sentiment Analysis Based on User Opinion Mining,” Global Journal of Engineering Science and Research Management, vol. 5, no. 8, pp. 58–63, 2018. 3. {Kavitha2024} P. Kavitha, “Opinion Mining Based Fake News Detection in Twitter Sentiment Analysis Using Lexical Cross Mutation Deep Vectorized Convolution Neural Network,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 1, pp. 1–7, 2024. 4.{Mittal2019} A. Mittal, “Sentiment Analysis on Twitter Data: A Survey,” \textit{ACM Computing Surveys}, vol. 52, no. 3, pp. 1–35, 2019. 5.{Rodriguez2023} M. Rodríguez-Ibáñez, “A Review on Sentiment Analysis of Twitter Data,” International Journal of Computer Applications, vol. 185, no. 28, pp. 1–6, 2023. 6.{Srivastava2024} A. Srivastava, “A Review on Sentiment Analysis of Twitter Data Using Machine Learning Techniques,” International Journal of Engineering and Management Research, vol. 12, no. 1, pp. 1–7, 2024. 7. {Chaudhary2023} L. Chaudhary, “A Review of Deep Learning Models for Twitter Sentiment Analysis,” Journal of Computer Science and Technology, vol. 38, no. 2, pp. 1–15, 2023. 8.{Tan2023} K. L. Tan, “A Survey of Sentiment Analysis: Approaches, Datasets, and Applications,” MDPI Electronics, vol. 13, no. 7, pp. 1–23, 2023.

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