Using Dependency Bigrams and Discourse Connectives for Predicting the Helpfulness of Online Reviews

Mertz, Mathias, Korfiatis, Nikolaos ORCID: and Zicari, Roberto V (2014) Using Dependency Bigrams and Discourse Connectives for Predicting the Helpfulness of Online Reviews. In: E-Commerce and Web Technologies. Springer, pp. 146-152. ISBN 978-3-319-10490-4

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Helpfulness prediction of online consumer reviews is an interesting research topic with immediate practical applications both from a data mining and marketing perspective. As such a set of studies have been published in the last few years to tackle this problem, targeting the reviews' textual characteristics. In this paper, we propose and evaluate two text-based features that have not been used in the context of consumer review helpfulness prediction before. The first considers a variation of the bigram feature, utilizing grammatical dependencies instead of word adjacency. The second captures the type and amount of discourse in a text by looking for discourse connectives. In our experiments, we treat the helpfulness prediction problem as a binary classification task. The results show that both features contain valuable information for evaluating review helpfulness, however they should be used with caution due to the restrictive experimental setup. The study serves as a ground for future work regarding the usefulness of the proposed features in that perspective.

Item Type: Book Section
Faculty \ School: Faculty of Social Sciences > Norwich Business School
UEA Research Groups: Faculty of Social Sciences > Research Groups > Innovation, Technology and Operations Management
Faculty of Social Sciences > Research Centres > Centre for Competition Policy
Depositing User: Pure Connector
Date Deposited: 20 Jan 2015 15:36
Last Modified: 19 Apr 2023 01:26
DOI: 10.1007/978-3-319-10491-1_15

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