3,450 research outputs found
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
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
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 reviewsā 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 eļ¬ectiveness of the existing approaches have not been systematically compared. To address these challenges, we ļ¬rst 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
Profiling users' behavior, and identifying important features of review 'helpfulness'
The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve
their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract peopleās attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation,
linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers
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., NaĆÆve 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
Exploring determinants of attraction and helpfulness of online product review:a consumer behaviour perspective
To assist filtering and sorting massive review messages, this paper attempts to examine the determinants of review attraction and helpfulness. Our analysis divides consumersā reading process into ānotice stageā and ācomprehend stageā and considers the impact of āexplicit informationā and āimplicit informationā of review attraction and review helpfulness. 633 online product reviews were collected from Amazon China. A mixed-method approach is employed to test the conceptual model proposed for examining the influencing factors of review attraction and helpfulness. The empirical results show that reviews with negative extremity, more words, and higher reviewer rank easily gain more attraction and reviews with negative extremity, higher reviewer rank, mixed subjective property, and mixed sentiment seem to be more helpful. The research findings provide some important insights, which will help online businesses to encourage consumers to write good quality reviews and take more active actions to maximise the value of online reviews
- ā¦