Stance Classification of Tweets Using Skip Char Ngrams
Abstract
In this research, we focus on automatic supervised stance classification of tweets. Given test datasets of tweets from five various topics, we try to classify the stance of the tweet authors as either in FAVOR of the target, AGAINST it, or NONE. We apply eight variants of seven supervised machine learning methods and three filtering methods using the WEKA platform. The macro-average results obtained by our algorithm are significantly better than the state-of-art results reported by the best macro-average results achieved in the SemEval 2016 Task 6-A for all the five released datasets. In contrast to the competitors of the SemEval 2016 Task 6-A, who did not use any char skip ngrams but rather used thousands of ngrams and hundreds of word embedding features, our algorithm uses a few tens of features mainly character-based features where most of them are skip char ngram features.
Cite
Text
HaCohen-Kerner et al. "Stance Classification of Tweets Using Skip Char Ngrams." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71273-4_22Markdown
[HaCohen-Kerner et al. "Stance Classification of Tweets Using Skip Char Ngrams." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/hacohenkerner2017ecmlpkdd-stance/) doi:10.1007/978-3-319-71273-4_22BibTeX
@inproceedings{hacohenkerner2017ecmlpkdd-stance,
title = {{Stance Classification of Tweets Using Skip Char Ngrams}},
author = {HaCohen-Kerner, Yaakov and Ido, Ziv and Ya'akobov, Ronen},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {266-278},
doi = {10.1007/978-3-319-71273-4_22},
url = {https://mlanthology.org/ecmlpkdd/2017/hacohenkerner2017ecmlpkdd-stance/}
}