[1]ZHAI Xueming,WEI Wei.Text sentiment analysis combining hybrid neural network and conditional random field[J].CAAI Transactions on Intelligent Systems,2021,16(2):202-209.[doi:10.11992/tis.201907041]
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Text sentiment analysis combining hybrid neural network and conditional random field

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 202-209 Column: 学术论文—机器学习 Public date: 2021-03-05
Title:
Text sentiment analysis combining hybrid neural network and conditional random field
Author(s):
ZHAI Xueming;  WEI Wei
School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
convolutional neural network (CNN); gated recurrent unit (GRU); conditional random field (CRF); text sentiment analysis; language model; semantic feature; contextual information; classifier
CLC:
TP391
DOI:
10.11992/tis.201907041
Abstract:
To solve problems such as the long training time of neural network models and insufficient contextual-information learning in text sentiment analysis, in this paper, we propose a model that combines a hybrid neural network with the conditional random field (CRF). Taking the neural network as the language model, the model combines the semantic information and structural features of the convolutional neural network with the bi-directional gated recurrent unit. The CRF model is used as a classifier that determines the probability distributions of emotions, from which it can then accurately determine the emotion category. The model was tested on the NLPCC 2014 data set, and achieved an accuracy rate of 91.74%. Compared with other classification models, the proposed model can obtain better accuracy and F values.
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