Constructing Explainable Opinion Graphs from Review

• URL: http://arxiv.org/abs/2006.00119v2
• Date: Tue, 13 Apr 2021 23:04:40 GMT
• Title: Constructing Explainable Opinion Graphs from Review
• Authors: Nofar Carmeli and Xiaolan Wang and Yoshihiko Suhara and Stefanos Angelidis and Yuliang Li and Jinfeng Li and Wang-Chiew Tan
• Abstract summary: We present ExplainIt, a system that extracts and organizes opinions into an opinion graph. In such graphs, a node represents a set of semantically similar opinions extracted from reviews. We experimentally demonstrate that the explanation relationships generated in the opinion graph are of good quality.
• Score: 26.262465541048662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Web is a major resource of both factual and subjective information. While there are significant efforts to organize factual information into knowledge bases, there is much less work on organizing opinions, which are abundant in subjective data, into a structured format. We present ExplainIt, a system that extracts and organizes opinions into an opinion graph, which are useful for downstream applications such as generating explainable review summaries and facilitating search over opinion phrases. In such graphs, a node represents a set of semantically similar opinions extracted from reviews and an edge between two nodes signifies that one node explains the other. ExplainIt mines explanations in a supervised method and groups similar opinions together in a weakly supervised way before combining the clusters of opinions together with their explanation relationships into an opinion graph. We experimentally demonstrate that the explanation relationships generated in the opinion graph are of good quality and our labeled datasets for explanation mining and grouping opinions are publicly available.

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