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Review Papers | Computer Science | India | Volume 12 Issue 11, November 2023 | Popularity: 5.3 / 10

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Emotion in Text: A Survey of Sentiment Analysis Techniques and Applications

Shrinath Pai

Abstract: An important area of study in natural language processing is sentiment analysis, which is concerned with identifying and deciphering emotions from text. This paper emphasizes the intricate relationships that arise between words and feelings in diverse contexts and offers a thorough overview of sentiment analysis techniques. We examine the evolution of sentiment analysis methods, following their progression from traditional machine learning approaches to cutting-edge deep learning models. The machine learning algorithms, sentiment lexicons, and feature engineering methods that support sentiment analysis are all thoroughly examined in this survey. We also look at the use of deep neural networks, a recent trend, and how well they can extract subtle emotional nuances from text. We address the challenges posed by sarcasm, context, and cultural variations, providing an appreciation of the limitations and benefits of the sentiment analysis frameworks currently in use. This paper also examines the many uses of sentiment analysis, such as sentiment-aware recommendation systems, social media monitoring, and customer feedback analysis. We examine the ways in which sentiment analysis is being applied to new sectors, such as finance, healthcare, and other areas, highlighting the revolutionary potential of this approach to decision-making. The paper concludes with a discussion of unresolved research issues and potential directions for emotion-centric sentiment analysis, emphasizing the significance of continuing ethical issues and language landscape adaptation. The goal of this investigation is to give academics, professionals, and hobbyists a thorough grasp of sentiment analysis methods and the various ways in which they are used in today's data-driven society.

Keywords: Sentiment Analysis, Machine Translation, Analysis, Opinion Mining, Emotion in Text

Edition: Volume 12 Issue 11, November 2023

Pages: 1880 - 1885

DOI: https://www.doi.org/10.21275/SR231126172649

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