徳島大学 教育・研究者情報データベース(EDB)

1. 松本 和幸([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.知能情報コース.情報工学講座])

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2. 任 福継(->個人[中川 福継])

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3. (英) Matsuoka Masaya / (日) 松岡 雅也 / (読) まつおか まさや

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4. 吉田 稔([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.知能情報コース.情報工学講座])

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5. 北 研二

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(英) Slang Feature Extraction by Analyzing Topic Change on Social Media

(英) Recently, we often see words such as youth slang, neologism and Internet slang on social networking sites (SNSs) that are not registered on dictionaries. Because the documents posted to SNSs include a lot of fresh information, they are thought to be useful for collecting information. It is important to analyze these words (hereinafter referred to as slang) and capture their features for the improvement of the accuracy of automatic information collection. This paper aims to analyze what features can be observed in slang by focusing on the topic. We construct topic models from document groups including target slang on Twitter by Latent Dirichlet Allocation (LDA). With the models, we chronologically analyze change of topics during a certain period of time to find out the difference in the features between slang and general words. Then, we propose a slang classification method based on the change of features.

1. (英) Slang
2. (英) Topic analysis
3. (英) Latent Dirichlet Allocation
4. (英) Social media,
5. (英) Tweet representation