Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review

• URL: http://arxiv.org/abs/2305.14842v1
• Date: Wed, 24 May 2023 07:48:41 GMT
• Title: Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review
• Authors: Karthick Prasad Gunasekaran
• Abstract summary: Sentiment analysis (SA) is the automated process of detecting and understanding the emotions conveyed through written text. SA has gained significant popularity in the field of Natural Language Processing (NLP) This study aims to enhance the efficiency and accuracy of SA processes, leading to smoother and error-free outcomes.
• Score: 0.15229257192293202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sentiment analysis (SA) is the automated process of detecting and understanding the emotions conveyed through written text. Over the past decade, SA has gained significant popularity in the field of Natural Language Processing (NLP). With the widespread use of social media and online platforms, SA has become crucial for companies to gather customer feedback and shape their marketing strategies. Additionally, researchers rely on SA to analyze public sentiment on various topics. In this particular research study, a comprehensive survey was conducted to explore the latest trends and techniques in SA. The survey encompassed a wide range of methods, including lexicon-based, graph-based, network-based, machine learning, deep learning, ensemble-based, rule-based, and hybrid techniques. The paper also addresses the challenges and opportunities in SA, such as dealing with sarcasm and irony, analyzing multi-lingual data, and addressing ethical concerns. To provide a practical case study, Twitter was chosen as one of the largest online social media platforms. Furthermore, the researchers shed light on the diverse application areas of SA, including social media, healthcare, marketing, finance, and politics. The paper also presents a comparative and comprehensive analysis of existing trends and techniques, datasets, and evaluation metrics. The ultimate goal is to offer researchers and practitioners a systematic review of SA techniques, identify existing gaps, and suggest possible improvements. This study aims to enhance the efficiency and accuracy of SA processes, leading to smoother and error-free outcomes.

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