# Using Argument-based Features to Predict and Analyse Review Helpfulness

arXiv: https://arxiv.org/abs/1707.07279 · HTML: https://mlsanity.com/paper/1707.07279/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions](https://mlsanity.com/paper/2211.03524/) (2022)
- [Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction](https://mlsanity.com/paper/2305.12678/) (2023)
- [On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction](https://mlsanity.com/paper/2303.00923/) (2023)
- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)
- [Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product Reviews](https://mlsanity.com/paper/2302.10199/) (2023)
- [Information Types in Product Reviews](https://mlsanity.com/paper/2502.14335/) (2025)

## Abstract (arXiv)

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.