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- W3100791639 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." @default.
- W3100791639 created "2020-11-23" @default.
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- W3100791639 date "2022-06-10" @default.
- W3100791639 modified "2023-09-23" @default.
- W3100791639 title "Microblog Sentiment Analysis Using User Similarity and Interaction-Based Social Relations" @default.
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- W3100791639 doi "https://doi.org/10.4018/978-1-6684-6303-1.ch100" @default.
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