HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media |
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| Institution: | 1. Graduate School of Information Science and Technology, Hokkaido University, Japan;2. Faculty of Information Science and Technology, Hokkaido University, Japan;3. RIKEN Center for Advanced Intelligence Project (AIP), Japan;4. Department of Computer Science, Kitami Institute of Technology, Japan;1. The Hong Kong Polytechnic University, Hong Kong, China;2. Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China;3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China;4. Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China;5. Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, China;1. Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin, China;2. Institute of AI, Shandong Computer Science Center(National Supercomputer Center in Jinan), QILU University of Technology, China;1. Information Studies, School of Humanities, University of Glasgow, Glasgow, UK;2. Information School, University of Sheffield, Sheffield, UK;3. Special Collections Service, University of Reading, Reading, UK;1. Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran;2. Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran |
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| Abstract: | In this paper we introduce HEMOS (Humor-EMOji-Slang-based) system for fine-grained sentiment classification for the Chinese language using deep learning approach. We investigate the importance of recognizing the influence of humor, pictograms and slang on the task of affective processing of the social media. In the first step, we collected 576 frequent Internet slang expressions as a slang lexicon; then, we converted 109 Weibo emojis into textual features creating a Chinese emoji lexicon. In the next step, by performing two polarity annotations with new “optimistic humorous type” and “pessimistic humorous type” added to standard “positive” and “negative” sentiment categories, we applied both lexicons to attention-based bi-directional long short-term memory recurrent neural network (AttBiLSTM) and tested its performance on undersized labeled data. Our experimental results show that the proposed method can significantly improve the state-of-the-art methods in predicting sentiment polarity on Weibo, the largest Chinese social network. |
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