Incorporating Text Specificity into a Convolutional Neural Network for the Classification of Review Perceived Helpfulness

Abstract

Reviews are valuable sources of information to support the decision making process. Therefore, the task of classifying reviews according to their helpfulness has paramount importance to facilitate the access of truly informative content. In this context, previous studies have unveiled several aspects and architectures that are beneficial for the task of review perceived helpfulness prediction. The present work aims to further investigate the influence of the text specificity aspect, defined as the level of details conveyed in a text, with the same purpose. First, we explore an unsupervised domain adaptation approach for assigning text specificity scores for sentences from product reviews and we propose an evaluation measure named Specificity Prediction Evaluation (SPE) in order to achieve more reliable specificity predictions. Then, we present domain-oriented guidelines on how to incorporate, into a CNN architecture, either hand-crafted features based on text specificity or the text specificity prediction task as an auxiliary task in a multitask learning setting. In the experiments, the perceived helpfulness classification models embodied with text specificity showed significant higher precision results in comparison to a popular SVM baseline.