| Autor: |
Zeeshan Anwar, Hammad Afzal, Taher Al-Shehari, Muna Al-Razgan, Taha Alfakih, Raheel Nawaz |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 65942-65955 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3397211 |
| Popis: |
Sentiment Analysis, a crucial tool for analyzing user opinions, has shown efficacy particularly when tailored to specific domains. While existing research predominantly focuses on training various classifiers for sentiment analysis within the software engineering (SE) domain, the outcomes often lack consistency when tested across different datasets. To address this gap, this paper proposes a novel approach utilizing transfer learning-based classifiers, fine-tuned and evaluated across diverse SE datasets. A comprehensive study is conducted, benchmarking machine learning and deep learning classifiers for SE sentiment analysis. Results indicate that transfer learning classifiers, namely GPT and BERT, outperform traditional approaches. Notably, the Bert large model achieves an F1-score of 0.89 on the Stack Overflow dataset, surpassing existing state-of-the-art tools. This research not only provides centralized insights but also paves the way for developing more accurate domain-specific sentiment analysis tools tailored for Software Engineering. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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