Attribute-Sentiment Pair Correlation Model Based on Online User Reviews

Abstract

With the popularization of Internet applications and the rapid development of e-commerce, online shopping has become a widespread and important pattern of consumption. Online user comments are an important data asset on e-commerce sites and have a great potential value for online users and merchants. However, accurate and effective extraction of the characteristics of products and users’ sentiment evaluation from a tremendous amount of comments is a significant challenge. Based on the concept of the LinLog energy model, this paper proposes an online review attribute-sentiment pair correlation model that evaluates user comments. After preprocessing the comment data of mobile phones and constructing an attribute dictionary, the proposed model conducts a clustering analysis of attributes and sentiment pairs to gain accurate assessment of attributes in order to explore potential information from user comments. Experiments conducted on one real-world dataset with comprehensive measurements verify the efficacy of the proposed model.

Hybrid Framework Integrating Lexicon and Learning Methods for Enhancing Sentiment Analysis Based on Patients' Tweets on Medicines

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Twitter Text Sentiment Analysis of Amazon Unlocked Mobile Reviews Using Supervised Learning Techniques

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