Abstract
In this paper, we face the problem of generating reputation for movies, products, hotels, restaurants and services by mining customer reviews expressed in natural language. To the best of our knowledge, previous studies on reputation generation for online entities have primarily examined semantic and sentiment orientation of customer reviews, disregarding other useful information that could be extracted from reviews, such as review helpfulness and review time. Therefore, we propose a new approach that combines review helpfulness, review time, review attached rating and review sentiment orientation for the purpose of generating a single reputation value toward various entities. The contribution of the paper is threefold. First, we design two equations to compute review helpfulness and review time scores, and we fine-tune Bidirectional Encoder Representations from Transformers (BERT) model to predict the review sentiment orientation probability. Second, we design a formula to assign a numerical score to each review. Then, we propose a new formula to compute reputation value toward the target entity (movie, product, hotel, restaurant, service, etc). Finally, we propose a new form to visualize reputation that depicts numerical reputation value, opinion categories, top positive review and top negative review. Experimental results coming from several real-world data sets of miscellaneous domains collected from IMDb, TripAdvisor and Amazon websites show the effectiveness of the proposed method in generating and visualizing reputation compared to three state-of-the-art reputation systems.
Highlights
The exponential growth of Web 2.0 has dramatically impacted the evolution of e-commerce platforms [1]–[4]
This study addressed the following research question: with the consideration of review helpfulness, review time, review sentiment orientation probability and review attached rating, can the proposed reputation system offer better results in terms of reputation generation and visualization than the previous reputation systems?
We propose a novel system that incorporates review time, review helpfulness, review sentiment orientation and review attached rating for the purpose of generating a numerical reputation value toward various entities
Summary
The exponential growth of Web 2.0 has dramatically impacted the evolution of e-commerce platforms [1]–[4]. We propose a reputation system that incorporates all these attributes during the process of generating and visualizing reputation for various entities (movies, products, hotels, restaurants and services) In this manner, this study addressed the following research question: with the consideration of review helpfulness, review time, review sentiment orientation probability and review attached rating, can the proposed reputation system offer better results in terms of reputation generation and visualization than the previous reputation systems (consider only semantic and sentiment relations)?. The contributions of this work are summarized as follows: Firstly, we propose a novel system that incorporates review time, review helpfulness, review sentiment orientation and review attached rating for the purpose of generating a numerical reputation value toward various entities (movies, products, hotels, restaurants, services, etc).
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