A graph convolutional network that combines user-item-comment relations with a comment similarity graph detects more spam at Xianyu than the deployed text-only baseline.
Aspect-Based Opinion Extraction from Customer reviews
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abstract
Text is the main method of communicating information in the digital age. Messages, blogs, news articles, reviews, and opinionated information abound on the Internet. People commonly purchase products online and post their opinions about purchased items. This feedback is displayed publicly to assist others with their purchasing decisions, creating the need for a mechanism with which to extract and summarize useful information for enhancing the decision-making process. Our contribution is to improve the accuracy of extraction by combining different techniques from three major areas, named Data Mining, Natural Language Processing techniques and Ontologies. The proposed framework sequentially mines products aspects and users opinions, groups representative aspects by similarity, and generates an output summary. This paper focuses on the task of extracting product aspects and users opinions by extracting all possible aspects and opinions from reviews using natural language, ontology, and frequent (tag) sets. The proposed framework, when compared with an existing baseline model, yielded promising results.
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Spam Review Detection with Graph Convolutional Networks
A graph convolutional network that combines user-item-comment relations with a comment similarity graph detects more spam at Xianyu than the deployed text-only baseline.