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

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.

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TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v7.i6.2019.761 ◽

2019 ◽

Vol 7 (6) ◽

pp. 77-83 ◽

Cited By ~ 1

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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Analysis and evaluation of the regional air quality index forecasting based on web-text sentiment analysis method

Environmental Impact Assessment Review ◽

10.1016/j.eiar.2020.106514 ◽

2021 ◽

Vol 87 ◽

pp. 106514

Author(s):

Kun Gao ◽

Prathik Anandhan ◽

Rakesh Kumar

Keyword(s):

Air Quality ◽

Sentiment Analysis ◽

Quality Index ◽

Air Quality Index ◽

Analysis Method ◽

Analysis And Evaluation ◽

Regional Air Quality ◽

Text Sentiment Analysis

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Text sentiment analysis method based on attention word vector

2020 International Conference on Modern Education and Information Management (ICMEIM) ◽

10.1109/icmeim51375.2020.00117 ◽

2020 ◽

Author(s):

Zhigang Xu ◽

Kai Dong ◽

Honglei Zhu

Keyword(s):

Sentiment Analysis ◽

Analysis Method ◽

Text Sentiment Analysis

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An Efficient Long Chinese Text Sentiment Analysis Method Using BERT-Based Models with BiGRU

2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) ◽

10.1109/cscwd49262.2021.9437789 ◽

2021 ◽

Author(s):

Deming Sheng ◽

Jingling Yuan

Keyword(s):

Sentiment Analysis ◽

Chinese Text ◽

Analysis Method ◽

Text Sentiment Analysis

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Weibo Text Sentiment Analysis Based on BERT and Deep Learning

Applied Sciences ◽

10.3390/app112210774 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10774

Author(s):

Hongchan Li ◽

Yu Ma ◽

Zishuai Ma ◽

Haodong Zhu

Keyword(s):

Deep Learning ◽

Public Opinion ◽

Sentiment Analysis ◽

Monitoring Network ◽

Feature Representation ◽

Vector Representation ◽

Text Data ◽

Proposed Model ◽

Sentiment Dictionary ◽

Text Sentiment Analysis

With the rapid increase of public opinion data, the technology of Weibo text sentiment analysis plays a more and more significant role in monitoring network public opinion. Due to the sparseness and high-dimensionality of text data and the complex semantics of natural language, sentiment analysis tasks face tremendous challenges. To solve the above problems, this paper proposes a new model based on BERT and deep learning for Weibo text sentiment analysis. Specifically, first using BERT to represent the text with dynamic word vectors and using the processed sentiment dictionary to enhance the sentiment features of the vectors; then adopting the BiLSTM to extract the contextual features of the text, the processed vector representation is weighted by the attention mechanism. After weighting, using the CNN to extract the important local sentiment features in the text, finally the processed sentiment feature representation is classified. A comparative experiment was conducted on the Weibo text dataset collected during the COVID-19 epidemic; the results showed that the performance of the proposed model was significantly improved compared with other similar models.

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