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Sentiment Analysis of User Reactions to Meta's Threads Launch and Twitter's X Renaming: A Comparative Study Using DistilBERT and Machine Learning

Sentiment Analysis of User Reactions to Meta's Threads Launch and Twitter's X Renaming: A Comparative Study Using DistilBERT and Machine Learning
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Author(s): Anukansha Sharma (Amity University, Noida, India), Ronit Bali (Amity University, Noida, India), Piyush Kumar (Amity University, Noida, India), G. M. Nandana (Amity University, Noida, India)and Shuchi Mala (Amity University, Noida, India)
Copyright: 2024
Pages: 21
Source title: Data-Driven Business Intelligence Systems for Socio-Technical Organizations
Source Author(s)/Editor(s): Pantea Keikhosrokiani (University of Oulu, Finland)
DOI: 10.4018/979-8-3693-1210-0.ch015

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Abstract

The massive dataset generated by social media platforms has attracted researchers interested in sentiment analysis. Natural language processing (NLP) has become popular for retrieving useful knowledge from user-generated content. In this chapter, the authors conducted a sentiment analysis of comments from Meta's new product, Threads, in competition to Twitter. Authors have tested the system with real-time comments. A dataset of 12,000 comments was extracted from YouTube using the YouTube API containing keywords such as “Threads,” “Twitter,” and “X.” The authors used DistilBERT to weigh the comments and segregate them into positive and negative. Machine learning algorithms were used to analyze the weighted data, and logistic regression was found to outperform random forest and KNN in providing an accuracy of 85.95%. The study highlights the importance of sentiment analysis in understanding user-generated content and its potential applications in the digital era.

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