Automatic Classification of Persuasive Arguments

Term project for ENGL 681 Introduction to Natural Language Processing at RIT, 2017.

@jdberlinski @glebpro

Abstract:

Public web forums allow for massive online debate, especially on the community platform Reddit. The language of persuasive arguments can be found on the sub-community /r/ChangeMyView, which encourages sharing views and discourse in a moderated public forum. To determine if there are any similarities between persuasive comments that were successful in changing a user's view we organized and labeled sets of argument examples, and found valuable features for classifying novel arguments through language modeling. Our model results saw 6% improved accuracy over the baseline, concluding that there are identifiable stylistic and topic features in effective arguments.

[Slides][Paper]

Technicals

Downloads

To download code: $ git clone https://github.com/glebpro/nlptermproject2017

To download corpus: download link. Data format explained here.

Scripts

To generate your own corpus, gathering posts backwards in time from now:

1. Populate corpus_utils/reddit.auth.json with your reddit credentials
2. Run $ python corpus_utils/download.py num_posts_to_collect

To generate comment pairs, use:

1. $ python corpus_utils/comment_pairs.py CMV_##.jsonlist

For the classifier:

1. To extract features: $ python model_files/get_features.py comment_pairs.jsonlist
2. To train new model: $ python model_files/model.py

• steps 1 and 2 might take hours

3. To explore results: $ python model_files/explore.py

• to print results from included model

Additional utility scrips included for parsing posts and comments

Requirements

python >= 3.4, praw >= 5.2, spacy >= 2.0.3, sklearn >= 0.18.1, numpy >= 1.13.3, nltk >= 3.2.5

License

MIT licensed. See the bundled LICENSE file for more details.