More Like this

1. Multi-criteria and Review-Based Overall Rating Prediction

   https://doi.org/10.1007/978-3-030-75765-6_38  

   Ceh-Varela, Edgar; Cao, Huiping; Le, Tuan (January 2021, Pacific-Asia Conference on Knowledge Discovery and Data Mining)

   An overall rating cannot reveal the details of user’s preferences toward each feature of a product. One widespread practice of e-commerce websites is to provide ratings on predefined aspects of the product and user-generated reviews. Most recent multi-criteria works employ aspect preferences of users or user reviews to understand the opinions and behavior of users. However, these works fail to learn how users correlate these information sources when users express their opinion about an item. In this work, we present Multi-task & Multi-Criteria Review-based Rating (MMCRR), a framework to predict the overall ratings of items by learning how users represent their preferences when using multi-criteria ratings and text reviews. We conduct extensive experiments with three real-life datasets and six baseline models. The results show that MMCRR can reduce prediction errors while learning features better from the data. 

   more » « less
2. The Removal of Irrelevant Human Factors in a Multi-Review Corpus through Text Filtering

   https://doi.org/10.54941/ahfe1003766  

   Moody, Aaron; Spurling, Makenzie; Hu, Chenyi (January 2023, Accelerating Open Access Science in Human Factors Engineering and Human-Centered Computing)

   Generating a high-quality explainable summary of a multi-review corpus can help people save time in reading the reviews. With natural language processing and text clustering, people can generate both abstractive and extractive summaries on a corpus containing up to 967 product reviews (Moody et al. 2022). However, the overall quality of the summaries needs further improvement. Noticing that online reviews in the corpus come from a diverse population, we take an approach of removing irrelevant human factors through pre-processing. Apply available pre-trained models together with reference based and reference free metrics, we filter out noise in each review automatically prior to summary generation. Our computational experiments evident that one may significantly improve the overall quality of an explainable summary from such a pre-processed corpus than from the original one. It is suggested of applying available high-quality pre-trained tools to filter noises rather than start from scratch. Although this work is on the specific multi-review corpus, the methods and conclusions should be helpful for generating summaries for other multi-review corpora. 

   more » « less
3. Review-Based Cross-Domain Collaborative Filtering: A Neural Framework

   Doan, T.N; Sahebi, S. (January 2019, Proceedings of the Third Workshop on Recommendation in Complex Scenarios)

   Cross-domain collaborative filtering recommenders exploit data from other domains (e.g., movie ratings) to predict users’ interests in a different target domain (e.g., suggest music). Most current cross-domain recommenders focus on modeling user ratings but pay limited attention to user reviews. Additionally, due to the complexity of these recommender systems, they cannot provide any information to users to support user decisions. To address these challenges, we propose Deep Hybrid Cross Domain (DHCD) model, a cross-domain neural framework, that can simultaneously predict user ratings, and provide useful information to strengthen the suggestions and support user decision across multiple domains. Specifically, DHCD enhances the predicted ratings by jointly modeling two crucial facets of users’ product assessment: ratings and reviews. To support decisions, it models and provides natural review-like sentences across domains according to user interests and item features. This model is robust in integrating user rating and review information from more than two domains. Our extensive experiments show that DHCD can significantly outperform advanced baselines in rating predictions and review generation tasks. For rating prediction tasks, it outperforms cross-domain and single-domain collaborative filtering as well as hybrid recommender systems. Furthermore, our review generation experiments suggest an improved perplexity score and transfer of review information in DHCD. 

   more » « less
4. Inconsistency Investigation between Online Review Content and Ratings

   Shan, G.; Zhang, D.; Zhou, L.; Suo, L.; Lim, J.; Shi, C. (August 2018, Twenty-fourth Americas Conference on Information Systems)

   Despite the tremendous role of online consumer reviews (OCRs) in facilitating consumer purchase decision making, the potential inconsistency between product ratings and review content could cause the uncertainty and confusions of prospect consumers toward a product. This research is aimed to investigate such inconsistency so as to better assist potential consumers with making purchase decisions. First, this study extracted a reviewer’s sentiments from review text via sentiment analysis. Then, it examined the correlation and inconsistency between product ratings and review sentiments via Pearson correlation coefficients (PCC) and box plots. Next, we compared such inconsistency patterns between fake and authentic reviews. Based on an analysis of 24,539 Yelp reviews, we find that although the ratings and sentiments are highly correlated, the inconsistency between the two is more salient in fake reviews than in authentic reviews. The comparison also reveals different inconsistency patterns between the two types of reviews. 

   more » « less
5. Document-Level Multi-Aspect Sentiment Classification for Online Reviews of Medical Experts

   https://doi.org/10.1145/3357384.3357828  

   Tian Shi, Vineeth Rakesh (November 2019, ACM International Conference on Information and Knowledge Management (CIKM ’19),)

   In the era of big data, online doctor review platforms, which enable patients to give feedback to their doctors, have become one of the most important components in healthcare systems. On one hand, they help patients to choose their doctors based on the experience of others. On the other hand, they help doctors to improve the quality of their service. Moreover, they provide important sources for us to discover common concerns of patients and existing problems in clinics, which potentially improve current healthcare systems. In this paper, we systematically investigate the dataset from one of such review platform, namely, ratemds.com, where each review for a doctor comes with an overall rating and ratings of four different aspects. A comprehensive statistical analysis is conducted first for reviews, ratings, and doctors. Then, we explore the content of reviews by extracting latent topics related to different aspects with unsupervised topic modeling techniques. As the core component of this paper, we propose a multi-task learning framework for the document-level multi-aspect sentiment classification. This task helps us to not only recover missing aspect-level ratings and detect inconsistent rating scores but also identify aspect-keywords for a given review based on ratings. The proposed model takes both features of doctors and aspect-keywords into consideration. Extensive experiments have been conducted on two subsets of ratemds dataset to demonstrate the effectiveness of the proposed model. 

   more » « less