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Social media opinion summarization using emotion cognition and convolutional neural networks
Institution:1. School of Economics and Management, Nanjing University of Science &Technology, 200, Xiaolinwei Road, Nanjing, Jiangsu, 210094, China;2. College of Business, University of Alabama in Huntsville, AL, 35899, United States;3. School of Computing and Information Sciences, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA, 15260, United States;1. Department of Industrial Systems and Engineering, Indian Institute of Technology-Kharagpur, Kharagpur, 721302, India;2. Reliability Engineering Centre, Indian Institute of Technology-Kharagpur, Kharagpur 721302, India;1. Room 1103, Bldg 24, ANU College of Business and Economics, Copland Building 24, The Australian National University, ACT, 2601, Australia;2. Dean, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-051, United Arab Emirates;3. Chair of Marketing and Entrepreneurship, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-049, United Arab Emirates;1. Department of Convergence IT Engineering, Pohang University of Science and Technology, Republic of Korea;2. Department of Electrical Engineering, Pohang University of Science and Technology, Republic of Korea;3. Department of Industrial and Management Engineering, Pohang University of Science and Technology, Republic of Korea
Abstract:Quickly and accurately summarizing representative opinions is a key step for assessing microblog sentiments. The Ortony-Clore-Collins (OCC) model of emotion can offer a rule-based emotion export mechanism. In this paper, we propose an OCC model and a Convolutional Neural Network (CNN) based opinion summarization method for Chinese microblogging systems. We test the proposed method using real world microblog data. We then compare the accuracy of manual sentiment annotation to the accuracy using our OCC-based sentiment classification rule library. Experimental results from analyzing three real-world microblog datasets demonstrate the efficacy of our proposed method. Our study highlights the potential of combining emotion cognition with deep learning in sentiment analysis of social media data.
Keywords:Convolutional neural network  Deep learning  Sentiment analysis  Social media  Text mining
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