Abstract
Helpfulness prediction represents an interesting research topic with immediate practical applications both from a data mining and marketing perspective. In this study we evaluate the performance of two text-based features that have not been used in that context, namely (a) a variation of the bigram feature, utilizing grammatical dependencies and (b) discourse connectives. By treating helpfulness prediction as a binary classification task we show that both features contain valuable information but 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 review helpfulness prediction
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Mertz, M., Korfiatis, N., Zicari, R.V. (2014). Using Dependency Bigrams and Discourse Connectives for Predicting the Helpfulness of Online Reviews. In: Hepp, M., Hoffner, Y. (eds) E-Commerce and Web Technologies. EC-Web 2014. Lecture Notes in Business Information Processing, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-319-10491-1_15
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DOI: https://doi.org/10.1007/978-3-319-10491-1_15
Publisher Name: Springer, Cham
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