@InProceedings{liu-EtAl:2017:EMNLP20174,
  author    = {Liu, Haijing  and  Gao, Yang  and  Lv, Pin  and  Li, Mengxue  and  Geng, Shiqiang  and  Li, Minglan  and  Wang, Hao},
  title     = {Using Argument-based Features to Predict and Analyse Review Helpfulness},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1358--1363},
  abstract  = {We study the helpful product reviews identification problem in this paper. We
	observe that the evidence-conclusion discourse relations, also known as
	arguments, often appear in product reviews, and we hypothesise that some
	argument-based features, e.g. the percentage of argumentative sentences, the
	evidences-conclusions ratios, are good indicators of helpful reviews. To
	validate this hypothesis, we manually annotate arguments in 110 hotel reviews,
	and investigate the effectiveness of several combinations of argument-based
	features. Experiments suggest that, when being used together with the
	argument-based features, the state-of-the-art baseline features can enjoy a
	performance boost (in terms of F1) of 11.01\% in average.},
  url       = {https://www.aclweb.org/anthology/D17-1142}
}