Research on the prediction of English topic richness in the context of multimedia data
With the evolution of the Internet and multimedia technologies, delving deep into multimedia data for predicting topic richness holds significant practical implications in public opinion monitoring and data discourse power competition. This study introduces an algorithm for predicting English topic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-04-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1967.pdf |
| _version_ | 1797201543584284672 |
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| author | Jie Jiao Hanan Aljuaid |
| author_facet | Jie Jiao Hanan Aljuaid |
| author_sort | Jie Jiao |
| collection | DOAJ |
| description | With the evolution of the Internet and multimedia technologies, delving deep into multimedia data for predicting topic richness holds significant practical implications in public opinion monitoring and data discourse power competition. This study introduces an algorithm for predicting English topic richness based on the Transformer model, applied specifically to the Twitter platform. Initially, relevant data is organized and extracted following an analysis of Twitter’s characteristics. Subsequently, a feature fusion approach is employed to mine, extract, and construct features from Twitter blogs and users, encompassing blog features, topic features, and user features, which are amalgamated into multimodal features. Lastly, the combined features undergo training and learning using the Transformer model. Through experimentation on the Twitter topic richness dataset, our algorithm achieves an accuracy of 82.3%, affirming the efficacy and superior performance of the proposed approach. |
| first_indexed | 2024-04-24T07:49:13Z |
| format | Article |
| id | doaj.art-a9ed059c5b5147e2b87da5beeb7a9434 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-24T07:49:13Z |
| publishDate | 2024-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-a9ed059c5b5147e2b87da5beeb7a94342024-04-18T15:05:06ZengPeerJ Inc.PeerJ Computer Science2376-59922024-04-0110e196710.7717/peerj-cs.1967Research on the prediction of English topic richness in the context of multimedia dataJie Jiao0Hanan Aljuaid1Jiaozuo Normal College, Jiaozuo, ChinaComputer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), Riyadh, Saudi ArabiaWith the evolution of the Internet and multimedia technologies, delving deep into multimedia data for predicting topic richness holds significant practical implications in public opinion monitoring and data discourse power competition. This study introduces an algorithm for predicting English topic richness based on the Transformer model, applied specifically to the Twitter platform. Initially, relevant data is organized and extracted following an analysis of Twitter’s characteristics. Subsequently, a feature fusion approach is employed to mine, extract, and construct features from Twitter blogs and users, encompassing blog features, topic features, and user features, which are amalgamated into multimodal features. Lastly, the combined features undergo training and learning using the Transformer model. Through experimentation on the Twitter topic richness dataset, our algorithm achieves an accuracy of 82.3%, affirming the efficacy and superior performance of the proposed approach.https://peerj.com/articles/cs-1967.pdfTopic richnessMulti-modal features extractionTransformerMultimedia data |
| spellingShingle | Jie Jiao Hanan Aljuaid Research on the prediction of English topic richness in the context of multimedia data PeerJ Computer Science Topic richness Multi-modal features extraction Transformer Multimedia data |
| title | Research on the prediction of English topic richness in the context of multimedia data |
| title_full | Research on the prediction of English topic richness in the context of multimedia data |
| title_fullStr | Research on the prediction of English topic richness in the context of multimedia data |
| title_full_unstemmed | Research on the prediction of English topic richness in the context of multimedia data |
| title_short | Research on the prediction of English topic richness in the context of multimedia data |
| title_sort | research on the prediction of english topic richness in the context of multimedia data |
| topic | Topic richness Multi-modal features extraction Transformer Multimedia data |
| url | https://peerj.com/articles/cs-1967.pdf |
| work_keys_str_mv | AT jiejiao researchonthepredictionofenglishtopicrichnessinthecontextofmultimediadata AT hananaljuaid researchonthepredictionofenglishtopicrichnessinthecontextofmultimediadata |