An empirical study of sentiment analysis utilizing machine learning and deep learning algorithms
Journal of Computational Social Science, cilt.7, sa.1, ss.241-257, 2024 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 7 Sayı: 1
- Basım Tarihi: 2024
- Doi Numarası: 10.1007/s42001-023-00236-5
- Dergi Adı: Journal of Computational Social Science
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
- Sayfa Sayıları: ss.241-257
- Anahtar Kelimeler: Deep learning, Machine learning, Sentiment analysis, Text mining
- Ankara Üniversitesi Adresli: Hayır
Özet
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.