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

Author

Listed:
  • Betul Erkantarci

    (Abdullah Gul University)

  • Gokhan Bakal

    (Abdullah Gul University)

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.

Suggested Citation

  • Betul Erkantarci & Gokhan Bakal, 2024. "An empirical study of sentiment analysis utilizing machine learning and deep learning algorithms," Journal of Computational Social Science, Springer, vol. 7(1), pages 241-257, April.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-023-00236-5
    DOI: 10.1007/s42001-023-00236-5
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