Extended Multi-modality Features and Deep Learning Based Microblog Short Text Sentiment Analysis

SUN Xiao; PENG Xiaoqi; HU Min; REN Fuji

doi:10.11999/JEIT160975

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2048-2055

SUN Xiao, PENG Xiaoqi, HU Min, REN Fuji. Extended Multi-modality Features and Deep Learning Based Microblog Short Text Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2048-2055. doi: 10.11999/JEIT160975

Citation:

SUN Xiao, PENG Xiaoqi, HU Min, REN Fuji. Extended Multi-modality Features and Deep Learning Based Microblog Short Text Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2048-2055. doi: 10.11999/JEIT160975

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Extended Multi-modality Features and Deep Learning Based Microblog Short Text Sentiment Analysis

doi: 10.11999/JEIT160975

1.
(Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei 230009, China)
2.

Funds:

The National Natural Science Foundation of China (61432004), The Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (201407345), The Natural Science Foundation of Anhui Province (1508085QF119), The China Postdoctoral Science Foundation (2015M580532)

Received Date: 2016-09-28
Rev Recd Date: 2017-05-17
Publish Date: 2017-09-19

Abstract

Abstract

This paper presents a Deep Belief Nets (DBN) model and a multi-modality feature extraction method to extend features, dimensionalities of short text for Chinese microblogging sentiment classification. Besides traditional features sets for document classification, comments for certain posts are also extracted as part of the microblogging features according to the relationship between commenters and posters through constructing microblogging social network as input information. Multi-modality features are combined and adopted as the input vector for DBN. A DBN model, which is stacked with several layers of Restricted Boltzmann Machine (RBM), is implemented to initialize the structure of neural network. The RBM layers can take probability distribution samples of input data to learn hidden syntactic structures for better feature representation. A Classification RBM (ClassRBM) layer, which is stacked on top of the former RBM layers, is adapted to achieve the final sentiment classification. The results demonstrate that, with proper structure and parameter, the performance of the proposed deep learning method on sentiment classification is better than the state of the art surface learning models such as SVM or NB, which proves that DBN is suitable for short-length document classification with the proposed feature dimensionality extension method.
- Social network,
- Deep Belief Nets (DBN),
- Extended multi-modality features,
- Restricted Boltzmann Machine (RBM),
- Classification restricted Boltzmann machine

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References(26)

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