Enhancing Polarity Classification in Social Media Texts: The Role of Emojis and Hashtags
Zubair,Samareen Zeng,Li
Zubair,Samareen
Zeng,Li
Abstract
This study investigates the often-dismissed potential of emojis and hashtags in enhancing sentiment analysis with social media data. Utilising data collected from an emerging decentralised platform Mastodon, we examine the roles of these text-element features and assess their impact on sentiment analysis performance. Employing three prevalent machine learning models (Support Vector Machine, Naive Bayes, and Random Forest), we design four experiments and compare the influence of text, hashtags, emojis, and their combination on polarity classification. The results consistently show that incorporating hashtags and emojis alongside text improves sentiment analysis accuracy. These findings emphasise the importance of recognizing the value of these text features in social media text analysis, challenging their common dismissal as inconsequential noise.
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2023-11-17
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Research Projects
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Citation
Zubair, S & Zeng, L 2023, Enhancing Polarity Classification in Social Media Texts: The Role of Emojis and Hashtags. in 2023 Annual Meeting of ASIS &T Asia Pacific Chapter.