Helpfulness Guided Review Summarization

User-generated online reviews are an important information resource in people's everyday life. As the review volume grows explosively, the ability to automatically identify and summarize useful information from reviews becomes essential in providing analytic services in many review-based applications. While prior work on review summarization focused on different review perspectives (e.g. topics, opinions, sentiment, etc.), the helpfulness of reviews is an important informativeness indicator that has been less frequently explored. In this thesis, we investigate automatic review helpfulness prediction and exploit review helpfulness for review summarization in distinct review domains. We explore two paths for predicting review helpfulness in a general setting: one is by tailoring existing helpfulness prediction techniques to a new review domain; the other is by using a general representation of review content that reflects review helpfulness across domains. For the first one, we explore educational peer reviews and show how peer-review domain knowledge can be introduced to a helpfulness model developed for product reviews to improve prediction performance. For the second one, we characterize review language usage, content diversity and helpfulness-related topics with respect to different content sources using computational linguistic features. For review summarization, we propose to leverage user-provided helpfulness assessment during content selection in two ways: 1) using the review-level helpfulness ratings directly to filter out unhelpful reviews, 2) developing sentence-level helpfulness features via supervised topic modeling for sentence selection. As a demonstration, we implement our methods based on an extractive multi-document summarization framework and evaluate them in three user studies. Results show that our helpfulness-guided summarizers outperform the baseline in both human and automated evaluation for camera reviews and movie reviews. While for educational peer reviews, the preference for helpfulness depends on student writing performance and prior teaching experience

Exploring Latent Semantic Factors to Find Useful Product Reviews

Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Identifying Features and Predicting Consumer Helpfulness of Product Reviews

Major corporations utilize data from online platforms to make user product or service recommendations. Companies like Netflix, Amazon, Yelp, and Spotify rely on purchasing trends, user reviews, and helpfulness votes to make content recommendations. This strategy can increase user engagement on a company\u27s platform. However, misleading and/or spam reviews significantly hinder the success of these recommendation strategies. The rise of social media has made it increasingly difficult to distinguish between authentic content and advertising, leading to a burst of deceptive reviews across the marketplace. The helpfulness of the review is subjective to a voting system. As such, this study aims to predict product reviews that are helpful and enable strategies to moderate a user review post to improve the helpfulness quality of a review. The prediction of review helpfulness will utilize NLP methods against Amazon product review data. Multiple machine learning principles of different complexities will be implemented in this review to compare the results and ease of implementation (e.g., Naïve Bayes and BERT) to predict a product review\u27s helpfulness. The result of this study concludes that review helpfulness can be effectively predicted through the deployment of model features. The removal of duplicate reviews, the imputing of review helpfulness based on word count, and the inclusion of lexical elements are recommended to be included in review analysis. The results of this research indicate that the deployment of these features results in a high F1-Score of 0.83 for predicting helpful Amazon product reviews