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SentiCR Initial Commit for ASE review May 14, 2017
cross-validation-results Initial Commit for ASE review May 14, 2017
posthoc Added Posthoc analysis results and simulation script. Jul 27, 2017
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README.md Added citation information Nov 5, 2017

README.md

SentiCR

SentiCR is an automated sentiment analysis tool for code review comments. SentiCR uses supervised learning algorithms to train models based on 1600 manually label code review comments (https://github.com/senticr/SentiCR/blob/master/SentiCR/oracle.xlsx). Features of SentiCR include:

  • Special preprocessing steps to exclude URLs and code snippets
  • Special preprocessing for emoticons
  • Preprocessing steps for contractions
  • Special handling of negation phrases through precise identification
  • Optimized for the SE domain

Performance

In our hundred ten-fold cross-validations, SentiCR achieved 83.03% accuracy (i.e., human level accuracy), 67.84% precision, 58.35% recall, and 0.62 f-score on a Gradient Boosting Tree based model. Details cross validation results are included here: https://github.com/senticr/SentiCR/tree/master/cross-validation-results

Cite

Ahmed, T. , Bosu, A., Iqbal, A. and Rahimi, S., "SentiCR: A Customized Sentiment Analysis Tool for Code Review Interactions", In Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering (NIER track).

@INPROCEEDINGS{Ahmed-et-al-SentiCR,

author = {Ahmed, Toufique and Bosu, Amiangshu and Iqbal, Anindya and Rahimi, Shahram},

title = {{SentiCR: A Customized Sentiment Analysis Tool for Code Review Interactions}},

year = {2017},

series = {ASE '17},

booktitle = {32nd IEEE/ACM International Conference on Automated Software Engineering (NIER track)}, }

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