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Techniques and Approaches for Sentiment Analysis in Social Media

Techniques and Approaches for Sentiment Analysis in Social Media
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Author(s): Mohamed Hammad (Menoufia University, Egypt & Prince Sultan University, Saudi Arabia), Noura A. Semary (Menoufia University, Egypt), Khalid Amin (Menoufia University, Egypt)and Wesam Ahmed (Information Technology Department, Hurghada University, Egypt)
Copyright: 2025
Pages: 16
Source title: Humanizing Technology With Emotional Intelligence
Source Author(s)/Editor(s): Subrata Tikadar (Amity University, Kolkata, India), Haipeng Liu (Coventry University, UK), Pronaya Bhattacharya (Amity University Kolkata, India)and Samit Bhattacharya (Indian Institute of Technology Guwahati, India)
DOI: 10.4018/979-8-3693-7011-7.ch020

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Abstract

The sentiment of a person (opinion) can be expressed through speech or writing in a specific natural language. Sentiment analysis (SA) often aims to identify the opinions of a writer or speaker on a particular topic or the general contextual polarity of a document. Sentiment analysis is widely employed in social media and reviews for a variety of purposes, such as customer service, political reviews, policymaking, marketing research, and decision-making. Machine learning (ML) approaches allow for the extraction of inferences from user interactions. Emotions are analyzed using a variety of machine learning approaches, such as deep learning (DL), supervised, semi-supervised, and unsupervised learning. In this chapter, various methodologies for sentiment classification are introduced in this most challenging area of sentiment analysis. This study gives academics a worldwide perspective on the analysis of feelings and its related domain, applications, and obstacles by providing an in-depth discussion of sentiment analysis methodologies.

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