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Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
https://tokushima-u.repo.nii.ac.jp/records/2009583
https://tokushima-u.repo.nii.ac.jp/records/2009583fb9ea037-e8e3-45dd-9bb8-aa02fd4ff5ad
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
app_11_22_10567.pdf (4.77 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||||||||||||
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| 公開日 | 2022-03-04 | |||||||||||||||||||||||||||||
| アクセス権 | ||||||||||||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||||||||||||
| 関連識別子 | https://doi.org/10.3390/app112210567 | |||||||||||||||||||||||||||||
| 関連名称 | 10.3390/app112210567 | |||||||||||||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||||||||||
| タイトル | ||||||||||||||||||||||||||||||
| タイトル | Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning | |||||||||||||||||||||||||||||
| 著者 |
アミタニ, レイシ
× アミタニ, レイシ
× 松本, 和幸
WEKO
311
× 吉田, 稔
WEKO
641
× 北, 研二
WEKO
94
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| 抄録 | ||||||||||||||||||||||||||||||
| 内容記述 | This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both the features extracted from the image and text as input and the buzz class (buzz/non-buzz) and the number of “likes (favorites)” and “retweets (RTs)” as output. The predictions made using a single feature of the text and image were compared with the predictions using a combination of multiple features. The differences between buzz and non-buzz features were analyzed based on the cosine similarity between the text and the image. The buzz class was correctly identified with a correctness rate of approximately 80% for all combinations of image and text features, with the combination of BERT and VGG16 providing the highest correctness rate. | |||||||||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||||||||
| 主題 | multi-task learning | |||||||||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||||||||
| 主題 | buzz classification | |||||||||||||||||||||||||||||
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| 主題 | social media | |||||||||||||||||||||||||||||
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| 主題 | trend analysis | |||||||||||||||||||||||||||||
| 書誌情報 |
en : Applied Sciences 巻 11, 号 22, p. 10567, 発行日 2021-11-10 |
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| 収録物識別子タイプ | ISSN | |||||||||||||||||||||||||||||
| 収録物識別子 | 20763417 | |||||||||||||||||||||||||||||
| 出版者 | ||||||||||||||||||||||||||||||
| 出版者 | MDPI | |||||||||||||||||||||||||||||
| 権利情報 | ||||||||||||||||||||||||||||||
| 権利情報 | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | |||||||||||||||||||||||||||||
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| 識別子 | 382998 | |||||||||||||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||||||||||||
