In recent years, sentiment analysis has been aware within software engineering domain. While automated sentiment analysis has long been suffering from doubt of accuracy, the tool performance is unstable when being applied on datasets other than the original dataset for evaluation. Researchers also have the disagreements upon if machine learning algorithms perform better than conventional lexicon and rule based approaches. In this paper, we looked into the factors in datasets that may affect the evaluation performance, also evaluated the popular machine learning algorithms in sentiment analysis, then proposed a novel structure for automated sentiment tool combines advantages from both approaches.

Benchmark testing, Machine learning, Sentiment analysis, Software engineering
dx.doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00185
17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
School of Computer Science

Shen, J. (Jingyi), Baysal, O, & Shafiq, M.O. (M. Omair). (2019). Evaluating the performance of machine learning sentiment analysis algorithms in software engineering. In Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 (pp. 1023–1030). doi:10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00185