Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews

Abstract

Predicting the helpfulness of product reviews is a key component of many ecommerce tasks such as review ranking and recommendation. However, previous work mixed review helpfulness prediction with those outer layer tasks. Using nontext features, it leads to less transferable models. This paper solves the problem from a new angle by hypothesizing that helpfulness is an internal property of text. Purely using review text, we isolate review helpfulness prediction from its outer layer tasks, employ two interpretable semantic features, and use human scoring of helpfulness as ground truth. Experimental results show that the two semantic features can accurately predict helpfulness scores and greatly improve the performance compared with using features previously used. Cross-category test further shows the models trained with semantic features are easier to be generalized to reviews of different product categories. The models we built are also highly interpretable and align well with human annotations.

Using Argument-based Features to Predict and Analyse Review Helpfulness

“…date (Liu et al, 2008), product rating (Kim et al, 2006) and product type (Mudambi, 2010)) and intrinsic features (e.g. semantic dictionaries (Yang et al, 2015) and emotional dictionaries (Martin and Pu, 2014)). Compared to external features, intrinsic features can provide some insights and explanations for the prediction results, and support better cross-domain generalisation.…”

Aspect Extraction from Product Reviews Using Category Hierarchy Information

“…One important task about online product review is to extract the properties of products, known as aspects. Aspect extraction has many applications, such as opinion mining (Liu, 2012;Liu et al, 2015), summerization (Bagheri et al, 2013;Hu and Liu, 2004), helpfulness prediction (Yang et al, 2016;Yang et al, 2015) and recommendation (Reschke et al, 2013;Jakob, 2011).…”

BEATnIk: an algorithm to Automatic generation of educational description of movies

“…There are also studies using supervised learning strategies to predict the text relevance [Xiong and Litman 2011, Zeng and Wu 2013, Yang et al 2015. Additionally, the use of regression algorithms consistently improves the prediction of helpfulness [Wan 2013].…”

“…Most of those works rely on supervised algorithms such as classification and regression [Xiong and Litman 2011, Zeng and Wu 2013, Yang et al 2015. However, the quality of results produced by supervised algorithms is dependent on the existence of a large, domain-dependent training dataset.…”