Using Argument-based Features to Predict and Analyse Review Helpfulness. / Liu, Haijing; Gao, Yang; Lv, Ping; Li, Mengxue; Geng, Shiqiang; Li, Minglan; Wang, Hao.

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark : Association for Computational Linguistics, 2017. p. 1358–1363.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

Using Argument-based Features to Predict and Analyse Review Helpfulness. / Liu, Haijing; Gao, Yang; Lv, Ping; Li, Mengxue; Geng, Shiqiang; Li, Minglan; Wang, Hao.

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark : Association for Computational Linguistics, 2017. p. 1358–1363.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Liu, H, Gao, Y, Lv, P, Li, M, Geng, S, Li, M & Wang, H 2017, Using Argument-based Features to Predict and Analyse Review Helpfulness. in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, pp. 1358–1363. https://doi.org/10.18653/v1/D17-1142

APA

Liu, H., Gao, Y., Lv, P., Li, M., Geng, S., Li, M., & Wang, H. (2017). Using Argument-based Features to Predict and Analyse Review Helpfulness. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 1358–1363). Association for Computational Linguistics. https://doi.org/10.18653/v1/D17-1142

Vancouver

Liu H, Gao Y, Lv P, Li M, Geng S, Li M et al. Using Argument-based Features to Predict and Analyse Review Helpfulness. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark: Association for Computational Linguistics. 2017. p. 1358–1363 https://doi.org/10.18653/v1/D17-1142

Author

Liu, Haijing ; Gao, Yang ; Lv, Ping ; Li, Mengxue ; Geng, Shiqiang ; Li, Minglan ; Wang, Hao. / Using Argument-based Features to Predict and Analyse Review Helpfulness. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark : Association for Computational Linguistics, 2017. pp. 1358–1363

BibTeX

@inproceedings{37b56e972cb440d7b0d30e6d93c668c2,
title = "Using Argument-based Features to Predict and Analyse Review Helpfulness",
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.",
author = "Haijing Liu and Yang Gao and Ping Lv and Mengxue Li and Shiqiang Geng and Minglan Li and Hao Wang",
year = "2017",
month = sep,
doi = "10.18653/v1/D17-1142",
language = "English",
pages = "1358–1363",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
publisher = "Association for Computational Linguistics",

}

RIS

TY - GEN

T1 - Using Argument-based Features to Predict and Analyse Review Helpfulness

AU - Liu, Haijing

AU - Gao, Yang

AU - Lv, Ping

AU - Li, Mengxue

AU - Geng, Shiqiang

AU - Li, Minglan

AU - Wang, Hao

PY - 2017/9

Y1 - 2017/9

N2 - 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.

AB - 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.

U2 - 10.18653/v1/D17-1142

DO - 10.18653/v1/D17-1142

M3 - Conference contribution

SP - 1358

EP - 1363

BT - Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

PB - Association for Computational Linguistics

CY - Copenhagen, Denmark

ER -