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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.
Ahmed et al. (Sun,) studied this question.