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Politeness Estimator for Microblogs (Pem)

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Pem is a set of pre-trained machine-learning models that predict (im-)politeness scores in texts. The models are trained on annotated microblogs and packaged into a simple Python class for easy use. Currently, two language-microblog pairs are supported:

  • English Twitter
  • Mandarin Chinese Weibo

The description of the data and training process is part of our paper published at CSCW 2020. Preprint: https://arxiv.org/pdf/2008.02449.pdf

Looking for source code of analyses, and/or PoliteLex itself? See src/.

The annotated corpora is available upon request.

Installation

  1. Install requirements: pip install -r requirements.txt
  2. If you wish to use LIWC (highly recommended for increased accuracy),
    1. Put your English LIWC .dic file to the same directory as LIWC2015_Dictionary.dic.
    2. Convert LIWC dictionary to long form liwc15.csv by running python prepare_liwc.py.
    3. Rename liwc15.csv to english_liwc15.csv and repeat this for other languages, if needed.
  3. Prepare also the EmoLex lexicon by python prepare_emolex.py. Both english_emolex.csv and chinese_emolex.csv should be generated.

Notice that, due to license concerns, we are unable to provide LIWC in this repository.

Usage

  1. Put microblogs in a CSV file as a column text. I have included two toy examples, tweets.csv for English Twitter and weibo.csv for Mandarin Weibo. If Chinese, please pre-segment/pre-tokenize posts by whitespace.

  2. In your code:

     from pem import Pem # `Pem` is short for "Politeness Estimator for Microblogs".
 pem = Pem(
     liwc_path           ='english_liwc15.csv', # or '' if LIWC is unavailable
     emolex_path         ='english_emolex.csv', 
     estimator_path      ='english_twitter_politeness_estimator.joblib', # or 'english_twitter_politeness_estimator_noLiwc.joblib' if LIWC is unavailable
     feature_defn_path   ='english_twitter_additional_features.pickle')
 pem.load('tweets.csv')
 pem.tokenize()
 pem.vectorize()
 print(pem.predict())

    or, for Mandarin Weibo posts:

     from pem import Pem # `Pem` is short for "Politeness Estimator for Microblogs".
 pem = Pem(
     liwc_path           ='chinese_liwc15.csv', # or '' if LIWC is unavailable
     emolex_path         ='chinese_emolex.csv', 
     estimator_path      ='chinese_weibo_politeness_estimator.joblib', # or 'chinese_weibo_politeness_estimator_noLiwc.joblib' if LIWC is unavailable
     feature_defn_path   ='chinese_weibo_additional_features.pickle')
 pem.load('weibo.csv')
 pem.tokenize()
 pem.vectorize()
 print(pem.predict())

We are actively working on understanding sources of bias in classifiers and currently, estimates between -0.5 and 0.5 are treated as neutral. Would love your feedback on how to make this classifer better. Reach out at myli at alumni dot upenn dot edu or sharathg at cis dot upenn dot edu.

Citation

@article{li2020cscw,
  title={Studying Politeness across Cultures Using English Twitter and Mandarin Weibo},
  author={Li, Mingyang and Hickman, Louis and Tay, Louis and Ungar, Lyle and Guntuku, Sharath Chandra},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  number={CSCW},  
  year={2020}
}

APA
Li, M., Hickman, L., Tay, L., Ungar, L., & Guntuku, S. C. (2020). Studying Politeness across Cultures Using English Twitter and Mandarin Weibo. Proceedings of the ACM on Human-Computer Interaction (CSCW)