Combining Convolutional Neural Networks and Word Topic Features for Chinese Short Text Sentiment Analysis

2018 ◽  
Author(s):  
Jianghao Lin ◽  
Yeli Gu ◽  
Yongmei Zhou ◽  
Aimin Yang ◽  
Jin Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Short Text ◽  
Text Sentiment Analysis

scholarly journals TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

2019 ◽  
Vol 7 (6) ◽  
pp. 77-83 ◽  
Author(s):  
Dr. C. Arunabala ◽  
P. Jwalitha ◽  
Soniya Nuthalapati
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sentiment Analysis ◽  
Expressive Power ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Analysis Method ◽  
Mapping Functions ◽  
Generalization Ability ◽  
Text Sentiment Analysis

The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM

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