An Approach of Fuzzy Relation Equation and Fuzzy-Rough Set for Multi-label Emotion Intensity Analysis

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• 关键词：Opinion mining ; Fuzzy relation equation ; Sentiment analysis ; Multi ; labeled emotion ; Emotion intensity ; Fuzzy ; rough set
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9645
• 期：1
• 页码：65-80
• 全文大小：1,034 KB
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• 作者单位：Chu Wang (16)
  Daling Wang (16) (17)
  Shi Feng (16) (17)
  Yifei Zhang (16) (17)
  
  16. School of Computer Science and Engineering, Northeastern University, Shenyang, China
  17. Key Laboratory of Medical Image Computing (Northeastern University), Ministry of Education, Shenyang, 110819, People’s Republic of China
  
• 丛书名：Database Systems for Advanced Applications
• ISBN：978-3-319-32055-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

There are a large number of subjective texts which contain people’s all kinds of sentiments and emotions in social media. Analyzing the sentiments and predicting the emotional expressions of human beings have been widely studied in academic communities and applied in commercial systems. However, most of the existing methods focus on single-label sentiment analysis, which means that only an exclusive sentiment orientation (negative, positive or neutral) or an emotion state (joy, hate, love, sorrow, anxiety, surprise, anger, or expect) is considered for a document. In fact, multiple emotions may be widely coexisting in one document, paragraph, or even sentence. Moreover, different words can express different emotion intensities in the text. In this paper, we propose an approach that combining fuzzy relation equation with fuzzy-rough set for solving the multi-label emotion intensity analysis problem. We first get the fuzzy emotion intensity of every sentiment word by solving a fuzzy relation equation, and then utilize an improved fuzzy-rough set method to predict emotion intensity for sentences, paragraphs, and documents. Compared with previous work, our proposed algorithm can simultaneously model the multi-labeled emotions and their corresponding intensities in social media. Experiments on a well-known blog emotion corpus show that our proposed multi-label emotion intensity analysis algorithm outperforms baseline methods by a large margin.