Proceedings ArticleDOI
Leveraging automated sentiment analysis in software engineering
Rakibul Islam,Minhaz F. Zibran +1 more
- pp 203-214
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TLDR
SentiStrength-SE, a tool for improved sentiment analysis especially designed for application in the software engineering domain, achieves 73.85% precision and 85% recall, which are significantly higher than a state-of-the-art sentiment analysis tool the authors compare with.Abstract:
Automated sentiment analysis in software engineering textual artifacts has long been suffering from inaccuracies in those few tools available for the purpose. We conduct an in-depth qualitative study to identify the difficulties responsible for such low accuracy. Majority of the exposed difficulties are then carefully addressed in developing SentiStrength-SE, a tool for improved sentiment analysis especially designed for application in the software engineering domain. Using a benchmark dataset consisting of 5,600 manually annotated JIRA issue comments, we carry out both quantitative and qualitative evaluations of our tool. SentiStrength-SE achieves 73.85% precision and 85% recall, which are significantly higher than a state-of-the-art sentiment analysis tool we compare with.read more
Citations
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Proceedings ArticleDOI
Sentiment analysis for software engineering: how far can we go?
TL;DR: This work 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, and found the results were negative.
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Sentiment Polarity Detection for Software Development
TL;DR: Senti4SD as mentioned in this paper is a classifier specifically trained to support sentiment analysis in developers' communication channels, which is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity.
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SentiCR: a customized sentiment analysis tool for code review interactions
TL;DR: SentiCR, a sentiment analysis tool especially designed for code review comments, is built and a model, trained using the Gradient Boosting Tree (GBT) algorithm, is found providing the highest mean accuracy, mean precision, and mean recall in identifying negative review comments.
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How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow
TL;DR: This paper provides evidence-based guidelines for writing effective questions on Stack Overflow that software engineers can follow to increase the chance of getting technical help and empirically confirmed community guidelines that suggest avoiding rudeness in question writing.
Proceedings ArticleDOI
A benchmark study on sentiment analysis for software engineering research
TL;DR: In this paper, the authors report a benchmark study to assess the performance and reliability of three sentiment analysis tools specifically customized for software engineering and offer a reflection on the open challenges, as they emerge from a qualitative analysis of misclassified texts.
References
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Proceedings ArticleDOI
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Minqing Hu,Bing Liu +1 more
TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Thumbs up? Sentiment Classiflcation using Machine Learning Techniques
TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Proceedings Article
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher,Alex Perelygin,Jean Y. Wu,Jason Chuang,Christopher D. Manning,Andrew Y. Ng,Christopher Potts +6 more
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Proceedings ArticleDOI
Thumbs up? Sentiment Classification using Machine Learning Techniques
TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.