Network text sentiment analysis method combining LDA text representation and GRU-CNN

2018 ◽  
Vol 23 (3-4) ◽  
pp. 405-412 ◽  
Author(s):  
Li-xia Luo
Keyword(s):  
Sentiment Analysis ◽  
Text Representation ◽  
Analysis Method ◽  
Text Sentiment Analysis

scholarly journals Sentiment Analysis Method of Network Text Based on Improved AT-BiGRU Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinxin Lu ◽  
Hong Zhang
Keyword(s):  
Sentiment Analysis ◽  
Current Method ◽  
Recurrent Network ◽  
Analysis Method ◽  
Large Space ◽  
Spelling Errors ◽  
Training Time ◽  
Proposed Model ◽  
Correct Spelling ◽  
Text Sentiment Analysis

In order to solve the problems existing in the current method of emotional analysis of network text, such as long training time, complex calculation, and large space cost, this paper proposes an Internet text sentiment analysis method based on the improved AT-BiGRU model. Firstly, the textblob package is imported to correct spelling errors before text preprocessing. Secondly, pad_sequences are used to fill in the input layer with a fixed length, the two-way gated recurrent network is used to extract information, and the attention mechanism is used to highlight the key information of the word vector. Finally, the GNU memory unit is transformed, and an improved BiGRU that can adapt to the recursive network structure is constructed. The proposed model is experimentally demonstrated on the SemEval-2014 Task 4 and SemEval-2017 Task 4 datasets. Experimental results show that the proposed model can effectively avoid the text sentiment analysis bias caused by spelling errors and prove the effectiveness of the improved AT-BiGRU model in terms of accuracy, loss rate, and iteration time.

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

scholarly journals A New Text Sentiment Analysis Method Based on Chinese Morphological Features and HowNet

2020 ◽  
Vol 1575 ◽  
pp. 012101
Author(s):  
Yan Cheng ◽  
Chunjia Liu ◽  
Yunhong Li ◽  
Linhui Zhong ◽  
Yue Feng
Keyword(s):  
Sentiment Analysis ◽  
Morphological Features ◽  
Analysis Method ◽  
Text Sentiment Analysis

Survey of text sentiment analysis

2013 ◽  
Vol 33 (6) ◽  
pp. 1574-1578 ◽  
Author(s):  
Ligong YANG ◽  
Jian ZHU ◽  
Shiping TANG
Keyword(s):  
Sentiment Analysis ◽  
Text Sentiment Analysis
Sign in / Sign up

Export Citation Format

Share Document