Name
Title | ||
|---|---|---|
Evaluating The Performance Of Machine Learning Sentiment Analysis Algorithms In Software Engineering |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00185 | IEEE 17TH INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP / IEEE 17TH INT CONF ON PERVAS INTELLIGENCE AND COMP / IEEE 5TH INT CONF ON CLOUD AND BIG DATA COMP / IEEE 4TH CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH) |
Keywords | Field | DocType |
Sentiment analysis, Machine learning, Benchmark testing, Software engineering | Rule-based system,Software engineering,Sentiment analysis,Computer science,Algorithm,Lexicon,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyi Shen | 1 | 1 | 1.02 |
| Olga Baysal | 2 | 1 | 0.34 |
| M. Omair Shafiq | 3 | 139 | 18.59 |