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A review of sentiment analysis: tasks, applications, and deep learning techniques

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Abstract

Sentiment analysis, a transformative force in natural language processing, revolutionizes diverse fields such as business, social media, healthcare, and disaster response. This review delves into the intricate landscape of sentiment analysis, exploring its significance, challenges, and evolving methodologies. We examine crucial aspects like dataset selection, algorithm choice, language considerations, and emerging sentiment tasks. The suitability of established datasets (e.g., IMDB Movie Reviews, Twitter Sentiment Dataset) and deep learning techniques (e.g., BERT) for sentiment analysis is explored. While sentiment analysis has made significant strides, it faces challenges such as deciphering sarcasm and irony, ensuring ethical use, and adapting to new domains. We emphasize the dynamic nature of sentiment analysis, encouraging further research to unlock the nuances of human sentiment expression and promote responsible and impactful applications across industries and languages.

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  1. Department of Computer Science and Mathematics, School of Science and Technology, The University of Fiji, Lautoka, Fiji

    Neeraj Anand Sharma & A. B. M. Shawkat Ali

  2. School of Computing, Mathematics, and Engineering, Charles Sturt University, Bathurst, NSW, 2795, Australia

    Muhammad Ashad Kabir

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All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Neeraj Anand Sharma, Professor ABM Shawkat Ali, and Associate Professor Muhammad Ashad Kabir. The first draft of the manuscript was written by Neeraj Anand Sharma and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Sharma, N.A., Ali, A.B.M.S. & Kabir, M.A. A review of sentiment analysis: tasks, applications, and deep learning techniques. Int J Data Sci Anal (2024). https://doi.org/10.1007/s41060-024-00594-x

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