Constructing Explainable Opinion Graphs from Review
This work addresses the need for structured opinion organization in subjective data, such as reviews, to support applications like explainable summaries and opinion search, representing an incremental advancement in opinion mining.
The paper tackles the problem of organizing subjective opinions from web reviews into a structured format by introducing ExplainIt, a system that constructs opinion graphs where nodes represent similar opinions and edges indicate explanatory relationships, with experimental results showing good quality in these relationships and publicly available labeled datasets.
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.