Using Argument-based Features to Predict and Analyse Review Helpfulness
Computation and Language
2017-07-25 v1
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.
Cite
@article{arxiv.1707.07279,
title = {Using Argument-based Features to Predict and Analyse Review Helpfulness},
author = {Haijing Liu and Yang Gao and Pin Lv and Mengxue Li and Shiqiang Geng and Minglan Li and Hao Wang},
journal= {arXiv preprint arXiv:1707.07279},
year = {2017}
}
Comments
6 pages, EMNLP2017