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Implementing Sentiment Analysis for Evaluating Stakeholder Perception and Social Impact
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Author(s): P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India), S. Poorani (Kongu Engineering College, India), R. Sahith (CVR College of Engineering, India), Ganesh Pandit Pathak (Sri Balaji University, Pune, India), Nilesh Anute (Sri Balaji University, Pune, India)and T. C. Manjunath (Rajarajeswari College of Engineering, India)
Copyright: 2026
Pages: 28
Source title:
Data-Driven ESG Strategy Implementation Through Business Intelligence
Source Author(s)/Editor(s): Markos Komodromos (University of Nicosia, Cyprus), Lambrini Seremeti (Agricultural University of Athens, Greece), Lazaros Anastasiadis (University of Crete, Greece), Panagiotis Liargovas (University of Peloponnese, Greece)and Sofia Anastasiadou (Univerity of Western Macedonia, Greece)
DOI: 10.4018/979-8-3373-5142-1.ch009
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Abstract
Sentiment analysis, often referred to as opinion mining, is a powerful technique within the broader field of natural language processing (NLP) and artificial intelligence (AI), designed to determine and quantify the emotional tone behind a body of text. Its core objective is to analyze written or spoken language and classify sentiments expressed as positive, negative, or neutral. In some sophisticated implementations, sentiment analysis can also identify more nuanced emotional states such as anger, joy, sadness, surprise, trust, and disgust. This technology has seen rapid adoption across numerous industries, driven by the explosion of online content and the growing need for organizations to understand public perception. The internet has enabled billions of users to share their opinions and experiences through social media platforms, product reviews, blog posts, news articles, forums, and other digital channels. Manually processing this immense volume of unstructured data is virtually impossible, which is where sentiment analysis provides invaluable support.
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