Microblog Sentiment Analysis Using User Similarity and Interaction-Based Social Relations

Chuanmin Mi, Xiaoyan Ruan and Lin Xiao
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Chuanmin Mi: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China
Xiaoyan Ruan: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Lin Xiao: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China

International Journal of Web Services Research (IJWSR), 2020, vol. 17, issue 3, 39-55

Abstract: With the rapid development of information technology, microblog sentiment analysis (MSA) has become a popular research topic extensively examined in the literature. Microblogging messages are usually short, unstructured, contain less information, creating a significant challenge for the application of traditional content-based methods. In this study, the authors propose a novel method, MSA-USSR, in which user similarity information and interaction-based social relations information are combined to build sentiment relationships between microblogging data. They make use of these microblog–microblog sentiment relations to train the sentiment polarity classification classifier. Two Sina-Weibo datasets were utilized to verify the proposed model. The experimental results show that the proposed method has a better sentiment classification accuracy and F1-score than the content-based support vector machine (SVM) method and the state-of-the-art supervised model known as SANT.

Date: 2020
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