| Item type |
文献 / Documents(1) |
| 公開日 |
2019-05-09 |
| 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/10.1049/trit.2018.1060 |
|
|
関連名称 |
10.1049/trit.2018.1060 |
| 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 |
Slang feature extraction by analysing topic change on social media |
| 著者 |
松本, 和幸
任, 福継
マツオカ, マサヤ
吉田, 稔
北, 研二
|
| 抄録 |
|
|
内容記述 |
Recently, the authors often see words such as youth slang, neologism and Internet slang on social networking sites (SNSs) that are not registered on dictionaries. Since the documents posted to SNSs include a lot of fresh information, they are thought to be useful for collecting information. It is important to analyse these words (hereinafter referred to as ‘slang’) and capture their features for the improvement of the accuracy of automatic information collection. This study aims to analyse what features can be observed in slang by focusing on the topic. They construct topic models from document groups including target slang on Twitter by latent Dirichlet allocation. With the models, they chronologically the analyse change of topics during a certain period of time to find out the difference in the features between slang and general words. Then, they propose a slang classification method based on the change of features. |
| 書誌情報 |
en : CAAI Transactions on Intelligence Technology
巻 4,
号 1,
p. 64-71,
発行日 2019-01-18
|
| 収録物ID |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
24682322 |
| 出版者 |
|
|
出版者 |
The Institution of Engineering and Technology |
| 権利情報 |
|
|
権利情報 |
This is an open access article published by the IET, Chinese Association for Artificial Intelligence and Chongqing University of Technology under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| EID |
|
|
識別子 |
349155 |
| 言語 |
|
|
言語 |
eng |
caai_4_1_64.pdf (571 KB)
.png)
