Abstract
We examine the extent to which we are able to automatically identify constructive online comments. We build several classifiers using New York Times Picks as positive examples and non-constructive thread comments from the Yahoo News Annotated Comments Corpus as negative examples of constructive online comments. We evaluate these classifiers on a crowd-annotated corpus containing 1,121 comments. Our best classifier achieves a top F1 score of 0.84.- Anthology ID:
- W17-4218
- Volume:
- Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Octavian Popescu, Carlo Strapparava
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 100–105
- Language:
- URL:
- https://aclanthology.org/W17-4218
- DOI:
- 10.18653/v1/W17-4218
- Cite (ACL):
- Varada Kolhatkar and Maite Taboada. 2017. Using New York Times Picks to Identify Constructive Comments. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 100–105, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Using New York Times Picks to Identify Constructive Comments (Kolhatkar & Taboada, 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W17-4218.pdf