Sentiment analysis for software engineering how far can we go?

Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers' emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results when used out-of-the-box, since they are not designed to...

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Vydané v:2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) s. 94 - 104
Hlavní autori: Lin, Bin, Zampetti, Fiorella, Bavota, Gabriele, Di Penta, Massimiliano, Lanza, Michele, Oliveto, Rocco
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 27.05.2018
Edícia:ACM Conferences
Predmet:
Information systems > Information retrieval > Retrieval tasks and goals > Sentiment analysis
Motion pictures
NLP
Sentiment analysis
Software
Software engineering
Task analysis
Training
sentiment analysis
software engineering
NLP
ISBN:9781450356381, 1450356389
ISSN:1558-1225
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Shrnutí:Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers' emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results when used out-of-the-box, since they are not designed to process SE datasets. The silver bullet for a successful application of sentiment analysis tools to SE datasets might be their customization to the specific usage context. We describe our experience in building a software library recommender exploiting developers' opinions mined from Stack Overflow. To reach our goal, we retrained---on a set of 40k manually labeled sentences/words extracted from Stack Overflow---a state-of-the-art sentiment analysis tool exploiting deep learning. Despite such an effort- and time-consuming training process, the results were negative. We changed our focus and performed a thorough investigation of the accuracy of commonly used tools to identify the sentiment of SE related texts. Meanwhile, we also studied the impact of different datasets on tool performance. Our results should warn the research community about the strong limitations of current sentiment analysis tools.
ISBN:9781450356381
1450356389
ISSN:1558-1225
DOI:10.1145/3180155.3180195