Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model
To solve the problems of polysemy and feature extraction in the text sentiment analysis process, a BERT-CNN-BiLSTM-Att hybrid model has been proposed for text sentiment analysis. The BERT pre-training model was established to break up the text input into words and obtain a dynamic word vector that w...
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10478509/ |
| _version_ | 1797231237543231488 |
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| author | Aixiang He Mideth Abisado |
| author_facet | Aixiang He Mideth Abisado |
| author_sort | Aixiang He |
| collection | DOAJ |
| description | To solve the problems of polysemy and feature extraction in the text sentiment analysis process, a BERT-CNN-BiLSTM-Att hybrid model has been proposed for text sentiment analysis. The BERT pre-training model was established to break up the text input into words and obtain a dynamic word vector that was then input into the CNN and the BiLSTM models respectively. Later, the local features of the word vector, extracted using CNN, and the global features, extracted using BiLSTM, were fused, and the key information of the Douban movie review dataset was highlighted using the attention mechanism to realize sentiment categorization of the dataset. The results of comparison between the constructed model and Word2Vec-BiLSTM, Word2Vec-CNN, Word2Vec-CNN-BiLSTM-Att, BERT, BERT-CNN and BERT-BiLSTM models show that the model that runs against the test dataset has an increased accuracy by 4.63%,4.37%,3.64%,2.63%,2.56% and 5.54% respectively. The experimental findings reveal that BERT-CNN-BiLSTM-Att’s sentiment analysis method is more accurate in performing sentiment classification. |
| first_indexed | 2024-04-24T15:41:11Z |
| format | Article |
| id | doaj.art-f05840bb28544cbcb22daf1322dde4ca |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T15:41:11Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f05840bb28544cbcb22daf1322dde4ca2024-04-01T23:00:46ZengIEEEIEEE Access2169-35362024-01-0112452294523710.1109/ACCESS.2024.338151510478509Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att ModelAixiang He0https://orcid.org/0009-0001-6320-3206Mideth Abisado1https://orcid.org/0000-0003-4215-7260College of Computing and Information Technologies, National University, Manila, PhilippinesCollege of Computing and Information Technologies, National University, Manila, PhilippinesTo solve the problems of polysemy and feature extraction in the text sentiment analysis process, a BERT-CNN-BiLSTM-Att hybrid model has been proposed for text sentiment analysis. The BERT pre-training model was established to break up the text input into words and obtain a dynamic word vector that was then input into the CNN and the BiLSTM models respectively. Later, the local features of the word vector, extracted using CNN, and the global features, extracted using BiLSTM, were fused, and the key information of the Douban movie review dataset was highlighted using the attention mechanism to realize sentiment categorization of the dataset. The results of comparison between the constructed model and Word2Vec-BiLSTM, Word2Vec-CNN, Word2Vec-CNN-BiLSTM-Att, BERT, BERT-CNN and BERT-BiLSTM models show that the model that runs against the test dataset has an increased accuracy by 4.63%,4.37%,3.64%,2.63%,2.56% and 5.54% respectively. The experimental findings reveal that BERT-CNN-BiLSTM-Att’s sentiment analysis method is more accurate in performing sentiment classification.https://ieeexplore.ieee.org/document/10478509/BERT-CNN-BiLSTM-Attsentiment analysishybrid modelfilm short text reviews comments |
| spellingShingle | Aixiang He Mideth Abisado Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model IEEE Access BERT-CNN-BiLSTM-Att sentiment analysis hybrid model film short text reviews comments |
| title | Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model |
| title_full | Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model |
| title_fullStr | Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model |
| title_full_unstemmed | Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model |
| title_short | Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model |
| title_sort | text sentiment analysis of douban film short comments based on bert cnn bilstm att model |
| topic | BERT-CNN-BiLSTM-Att sentiment analysis hybrid model film short text reviews comments |
| url | https://ieeexplore.ieee.org/document/10478509/ |
| work_keys_str_mv | AT aixianghe textsentimentanalysisofdoubanfilmshortcommentsbasedonbertcnnbilstmattmodel AT midethabisado textsentimentanalysisofdoubanfilmshortcommentsbasedonbertcnnbilstmattmodel |