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Most e-commerce websites (e.g., Amazon and TripAdvisor) show their users an initial set of useful product reviews. These reviews allow users to form a general idea about the product's characteristics. The usefulness of a review is mainly based on a score that the website users provide. Studies have shown that this score is not a good indicator of a review's actual helpfulness. Nonetheless, most past works still use it to classify a review as helpful or not. With the growing number of reviews, finding those helpful ones is a challenging task. In this work, we propose NovRev, a new unsupervised approach to recommend a personalized subset of unread useful reviews for those users looking to increase their knowledge about a product. NovRev considers an initial set of reviews as a context and recommends reviews that increase the product's information. We have extensively tested NovRev against five baseline methods, using eight real-life datasets from different product domains. The results show that NovRev can recommend novel, relevant, and diverse reviews while covering more information about the product.
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A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining
Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews. Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous. This paper proposes an approach to automatically mine and leverage domain-specific knowledge for classifying objects and attribute coreferences. The approach extracts domain-specific knowledge from unlabeled review data and trains a knowledge aware neural coreference classification model to leverage (useful) domain knowledge together with general commonsense knowledge for the task. Experimental evaluation on real world datasets involving five domains (product types) shows the effectiveness of the approach.
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- Award ID(s):
- 1910424
- PAR ID:
- 10302860
- Date Published:
- Journal Name:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
