An empirical study of sentiment analysis utilizing machine learning and deep learning algorithms

Betul Erkantarci () and Gokhan Bakal ()
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Betul Erkantarci: Abdullah Gul University
Gokhan Bakal: Abdullah Gul University

Journal of Computational Social Science, 2024, vol. 7, issue 1, No 10, 257 pages

Abstract: Abstract Among text-mining studies, one of the most studied topics is the text classification task applied in various domains, including medicine, social media, and academia. As a sub-problem in text classification, sentiment analysis has been widely investigated to classify often opinion-based textual elements. Specifically, user reviews and experiential feedback for products or services have been employed as fundamental data sources for sentiment analysis efforts. As a result of rapidly emerging technological advancements, social media platforms such as Twitter, Facebook, and Reddit, have become central opinion-sharing mediums since the early 2000s. In this sense, we build various machine-learning models to solve the sentiment analysis problem on the Reddit comments dataset in this work. The experimental models we constructed achieve F1 scores within intervals of 73–76%. Consequently, we present comparative performance scores obtained by traditional machine learning and deep learning models and discuss the results.

Keywords: Sentiment analysis; Machine learning; Deep learning; Text mining (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42001-023-00236-5

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