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2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) (2017)
Urbana, IL, USA
Oct. 30, 2017 to Nov. 3, 2017
ISBN: 978-1-5386-3976-4
pp: 106-111
Toufique Ahmed , Department of Computer Science & Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladeshi
Amiangshu Bosu , Department of Computer Science, Southern Illinois University Carbondale, IL, USA
Anindya Iqbal , Department of Computer Science & Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladeshi
Shahram Rahimi , Department of Computer Science, Southern Illinois University Carbondale, IL, USA
ABSTRACT
Sentiment Analysis tools, developed for analyzing social media text or product reviews, work poorly on a Software Engineering (SE) dataset. Since prior studies have found developers expressing sentiments during various SE activities, there is a need for a customized sentiment analysis tool for the SE domain. On this goal, we manually labeled 2000 review comments to build a training dataset and used our dataset to evaluate seven popular sentiment analysis tools. The poor performances of the existing sentiment analysis tools motivated us to build SentiCR, a sentiment analysis tool especially designed for code review comments. We evaluated SentiCR using one hundred 10-fold cross-validations of eight supervised learning algorithms. We found a model, trained using the Gradient Boosting Tree (GBT) algorithm, providing the highest mean accuracy (83%), the highest mean precision (67.8%), and the highest mean recall (58.4%) in identifying negative review comments.
INDEX TERMS
Tools, Sentiment analysis, Supervised learning, Training, Algorithm design and analysis, Dictionaries, Social network services
CITATION

T. Ahmed, A. Bosu, A. Iqbal and S. Rahimi, "SentiCR: A customized sentiment analysis tool for code review interactions," 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana, IL, USA, 2017, pp. 106-111.
doi:10.1109/ASE.2017.8115623
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