Social media opinion summarization using emotion cognition and convolutional neural networks

Peng Wu, Xiaotong Li, Si Shen and Daqing He

International Journal of Information Management, 2020, vol. 51, issue C

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 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:51:y:2020:i:c:s0268401218313690

DOI: 10.1016/j.ijinfomgt.2019.07.004

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