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Algorithms 2016, 9(2), 41; doi:10.3390/a9020041

Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks

1,†,* , 2,†
School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
School of Computer Science & Engineering, Dalian Nationalities University, Dalian 116600, China
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Tom Burr
Received: 16 February 2016 / Revised: 29 May 2016 / Accepted: 1 June 2016 / Published: 21 June 2016
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Sentiment analysis of online social media has attracted significant interest recently. Many studies have been performed, but most existing methods focus on either only textual content or only visual content. In this paper, we utilize deep learning models in a convolutional neural network (CNN) to analyze the sentiment in Chinese microblogs from both textual and visual content. We first train a CNN on top of pre-trained word vectors for textual sentiment analysis and employ a deep convolutional neural network (DNN) with generalized dropout for visual sentiment analysis. We then evaluate our sentiment prediction framework on a dataset collected from a famous Chinese social media network (Sina Weibo) that includes text and related images and demonstrate state-of-the-art results on this Chinese sentiment analysis benchmark. View Full-Text
Keywords: sentiment analysis; convolutional neural network; word vectors; microblog sentiment analysis; convolutional neural network; word vectors; microblog

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yu, Y.; Lin, H.; Meng, J.; Zhao, Z. Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks. Algorithms 2016, 9, 41.

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