Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks
The task of aspect-based sentiment analysis (ASBA) is to identify all the sentiment analyses expressed by specific aspect words in the text. How to identify specific objects (i.e., aspect words), describe the modifiers of the specific objects (i.e., opinion words), and judge the sentiment analysis e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/2221 |
| _version_ | 1797264770377711616 |
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| author | Jun Peng Baohua Su |
| author_facet | Jun Peng Baohua Su |
| author_sort | Jun Peng |
| collection | DOAJ |
| description | The task of aspect-based sentiment analysis (ASBA) is to identify all the sentiment analyses expressed by specific aspect words in the text. How to identify specific objects (i.e., aspect words), describe the modifiers of the specific objects (i.e., opinion words), and judge the sentiment analysis expressed by opinion words (sentimental classification) in one step has become a focus of research in ASBA. ASTE (Aspect Sentiment Triplet Extraction) based on DREN (Deep Relationship Enhancement Networks) has been proposed in this paper. It aims to extract the aspect words and opinion words in the review text in one-step. They can judge the sentiment analysis expressed by the opinion words. Therefore, the study defines ten kinds of word relations; then, the study uses the parts of the speech feature, syntactic feature, relative position feature and tree distance relative feature to enhance the word representation relationship, which enriches the table of information in the relational matrix. Secondly, based on the word representation of BERT and GCN, the structural information of the texts are extracted; then, further extraction of higher-level word semantic information and word relationship information through SWDA (Sliding Window Dilated Attention) occurs, as SWDA can capture the multi-granularity relationship in words. Finally, the experimental results show that the proposed method is effective. |
| first_indexed | 2024-04-25T00:34:11Z |
| format | Article |
| id | doaj.art-40bff53d114e4d8bafb15e701c5459a3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:34:11Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-40bff53d114e4d8bafb15e701c5459a32024-03-12T16:40:33ZengMDPI AGApplied Sciences2076-34172024-03-01145222110.3390/app14052221Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement NetworksJun Peng0Baohua Su1School of Education, City University of Macau, Macau 999078, ChinaSchool of Education, City University of Macau, Macau 999078, ChinaThe task of aspect-based sentiment analysis (ASBA) is to identify all the sentiment analyses expressed by specific aspect words in the text. How to identify specific objects (i.e., aspect words), describe the modifiers of the specific objects (i.e., opinion words), and judge the sentiment analysis expressed by opinion words (sentimental classification) in one step has become a focus of research in ASBA. ASTE (Aspect Sentiment Triplet Extraction) based on DREN (Deep Relationship Enhancement Networks) has been proposed in this paper. It aims to extract the aspect words and opinion words in the review text in one-step. They can judge the sentiment analysis expressed by the opinion words. Therefore, the study defines ten kinds of word relations; then, the study uses the parts of the speech feature, syntactic feature, relative position feature and tree distance relative feature to enhance the word representation relationship, which enriches the table of information in the relational matrix. Secondly, based on the word representation of BERT and GCN, the structural information of the texts are extracted; then, further extraction of higher-level word semantic information and word relationship information through SWDA (Sliding Window Dilated Attention) occurs, as SWDA can capture the multi-granularity relationship in words. Finally, the experimental results show that the proposed method is effective.https://www.mdpi.com/2076-3417/14/5/2221triplet extractionGraph Neural Networksattention mechanism |
| spellingShingle | Jun Peng Baohua Su Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks Applied Sciences triplet extraction Graph Neural Networks attention mechanism |
| title | Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks |
| title_full | Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks |
| title_fullStr | Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks |
| title_full_unstemmed | Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks |
| title_short | Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks |
| title_sort | aspect sentiment triplet extraction based on deep relationship enhancement networks |
| topic | triplet extraction Graph Neural Networks attention mechanism |
| url | https://www.mdpi.com/2076-3417/14/5/2221 |
| work_keys_str_mv | AT junpeng aspectsentimenttripletextractionbasedondeeprelationshipenhancementnetworks AT baohuasu aspectsentimenttripletextractionbasedondeeprelationshipenhancementnetworks |