Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
This paper describes the construction a short-text aspect-based sentiment analysis method based on Convolutional Neural Network (CNN) and Bidirectional Gating Recurrent Unit (BiGRU). The hybrid model can fully extract text features, solve the problem of long-distance dependence on the sequence, and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/5/2707 |
| _version_ | 1797475526499106816 |
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| author | Ziwen Gao Zhiyi Li Jiaying Luo Xiaolin Li |
| author_facet | Ziwen Gao Zhiyi Li Jiaying Luo Xiaolin Li |
| author_sort | Ziwen Gao |
| collection | DOAJ |
| description | This paper describes the construction a short-text aspect-based sentiment analysis method based on Convolutional Neural Network (CNN) and Bidirectional Gating Recurrent Unit (BiGRU). The hybrid model can fully extract text features, solve the problem of long-distance dependence on the sequence, and improve the reliability of training. This article reports empirical research conducted on the basis of literature research. The first step was to obtain the dataset and perform preprocessing, after which scikit-learn was used to perform TF-IDF calculations to obtain the feature word vector weight, obtain the aspect-level feature ontology words of the evaluated text, and manually mark the ontology of the reviewed text and the corresponding sentiment analysis polarity. In the sentiment analysis section, a hybrid model based on CNN and BiGRU (CNN + BiGRU) was constructed, which uses corpus sentences and feature words as the vector input and predicts the emotional polarity. The experimental results prove that the classification accuracy of the improved CNN + BiGRU model was improved by 12.12%, 8.37%, and 4.46% compared with the Convolutional Neural Network model (CNN), Long-Short Term Memory model (LSTM), and Convolutional Neural Network (C-LSTM) model. |
| first_indexed | 2024-03-09T20:46:22Z |
| format | Article |
| id | doaj.art-1ed364c7a87440269d42c6e7dfa4ab75 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:46:22Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1ed364c7a87440269d42c6e7dfa4ab752023-11-23T22:45:03ZengMDPI AGApplied Sciences2076-34172022-03-01125270710.3390/app12052707Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRUZiwen Gao0Zhiyi Li1Jiaying Luo2Xiaolin Li3School of Economics and Management, South China Normal University, Guangzhou 510006, ChinaSchool of Economics and Management, South China Normal University, Guangzhou 510006, ChinaSchool of Economics and Management, South China Normal University, Guangzhou 510006, ChinaSchool of Economics and Management, South China Normal University, Guangzhou 510006, ChinaThis paper describes the construction a short-text aspect-based sentiment analysis method based on Convolutional Neural Network (CNN) and Bidirectional Gating Recurrent Unit (BiGRU). The hybrid model can fully extract text features, solve the problem of long-distance dependence on the sequence, and improve the reliability of training. This article reports empirical research conducted on the basis of literature research. The first step was to obtain the dataset and perform preprocessing, after which scikit-learn was used to perform TF-IDF calculations to obtain the feature word vector weight, obtain the aspect-level feature ontology words of the evaluated text, and manually mark the ontology of the reviewed text and the corresponding sentiment analysis polarity. In the sentiment analysis section, a hybrid model based on CNN and BiGRU (CNN + BiGRU) was constructed, which uses corpus sentences and feature words as the vector input and predicts the emotional polarity. The experimental results prove that the classification accuracy of the improved CNN + BiGRU model was improved by 12.12%, 8.37%, and 4.46% compared with the Convolutional Neural Network model (CNN), Long-Short Term Memory model (LSTM), and Convolutional Neural Network (C-LSTM) model.https://www.mdpi.com/2076-3417/12/5/2707short textaspect-levelsentiment analysisconvolutional neural network (CNN)bidirectional gating recurrent unit (BiGRU) |
| spellingShingle | Ziwen Gao Zhiyi Li Jiaying Luo Xiaolin Li Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU Applied Sciences short text aspect-level sentiment analysis convolutional neural network (CNN) bidirectional gating recurrent unit (BiGRU) |
| title | Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU |
| title_full | Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU |
| title_fullStr | Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU |
| title_full_unstemmed | Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU |
| title_short | Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU |
| title_sort | short text aspect based sentiment analysis based on cnn bigru |
| topic | short text aspect-level sentiment analysis convolutional neural network (CNN) bidirectional gating recurrent unit (BiGRU) |
| url | https://www.mdpi.com/2076-3417/12/5/2707 |
| work_keys_str_mv | AT ziwengao shorttextaspectbasedsentimentanalysisbasedoncnnbigru AT zhiyili shorttextaspectbasedsentimentanalysisbasedoncnnbigru AT jiayingluo shorttextaspectbasedsentimentanalysisbasedoncnnbigru AT xiaolinli shorttextaspectbasedsentimentanalysisbasedoncnnbigru |