Context-aware Helpfulness Prediction for Online Product Reviews

Modeling and prediction of review helpfulness has become more predominant due to proliferation of e-commerce websites and online shops. Since the functionality of a product cannot be tested before buying, people often rely on different kinds of user reviews to decide whether or not to buy a product. However, quality reviews might be buried deep in the heap of a large amount of reviews. Therefore, recommending reviews to customers based on the review quality is of the essence. Since there is no direct indication of review quality, most reviews use the information that ''X out of Y'' users found the review helpful for obtaining the review quality. However, this approach undermines helpfulness prediction because not all reviews have statistically abundant votes. In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review. This model is based on convolutional neural network (CNN) and a context-aware encoding mechanism which can directly capture relationships between words irrespective of their distance in a long sequence. We validated our model on human annotated dataset and the result shows that our model significantly outperforms existing models for helpfulness prediction.Comment: Published as a proceeding paper in AIRS 201

HETEROGENEOUS GRAPH-BASED USER-SPECIFIC REVIEW HELPFULNESS PREDICTION

With the popularity of e-commerce and review websites, it is becoming increasingly important to identify the helpfulness of reviews. However, existing works on predicting reviews’ helpfulness have three major issues: (i) the correlation between helpfulness and features from review text is not clear yet, although many standard features are proposed, (ii) the relations between users, reviews and products have not been considered, (iii) the effectiveness of the existing approaches have not been systematically compared. To address these challenges, we first analyze the correlation between standard features and review helpfulness that are widely used in other work. Based on this analysis, we propose an end-to-end neural network architecture, the Global-Local Heterogeneous Graph Neural Networks (GL-HGNN). It consists of the graph construction and learning nodes representations both globally and locally. The graph is composed of three types of nodes including users, reviews and products, as well as four link types to build connections among these nodes. To better learn the feature representations, we employ a global graph neural network (GNN) branch and a local GNN branch on the whole graph and associated subgraphs to capture graph structure and information propagation. Finally, we provide an empirical comparison with traditional machine learning models training on hand-crafted features as well as four state-of-the-art deep learning models on eight Amazon product categories

Deep Learning Implementation for Comparison of User Reviews and Ratings

Sentiment Analysis is the task of identifying and classifying the sentiment expressed in a piece of text as of positive or negative sentiment and has wide application in E-Commerce. In present time, most e-commerce websites have product review sections, which can be used to identify customer satisfaction/dissatisfaction for their product. In E-COMMERCE websites such as Amazon.com, E-bay.com etc, consumers can submit their reviews along with a specific polarity rating (e.g. 1 to 5 stars at Amazon.com). There is a possibility of mismatch between review submitted and polarity of rating. For Amazon.com, a customer can submit a strongly positive review but give it a low rating. The objective of this thesis is to develop a web-service application which can be used to tackle this situation. We will perform Sentiment Analysis using Deep Learning on Amazon.com product review data. Product reviews will be converted to vectors using “PARAGRAPH VECTOR” which will later be used to train a Recurrent Neural Network with Gated Recurrent Unit. Our model will incorporate both semantic relationship of review text as well as product information. We have also devel- oped an application in Python, that will predict rating score for the submitted review using the trained model. If there is a mismatch between predicted rating score and submitted rating score, a warning/info will be provided