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Subjective? Emotional? Emotive?: Language Combinatorics based Automatic Detection of Emotionally Loaded Sentences
https://kitami-it.repo.nii.ac.jp/records/8545
https://kitami-it.repo.nii.ac.jp/records/8545780b46bc-8e12-4207-8640-3b0417fa5c15
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
LLS3-19307934.pdf (980.8 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2017-11-10 | |||||
| タイトル | ||||||
| タイトル | Subjective? Emotional? Emotive?: Language Combinatorics based Automatic Detection of Emotionally Loaded Sentences | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Affect Analysis | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Sentiment Analysis | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Pattern Extraction | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Language Modeling | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Emotive Expressions | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Language Combinatorics | |||||
| 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 | |||||
| アクセス権 | ||||||
| アクセス権 | open access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
| 著者 |
Michal, Ptaszynski
× Michal, Ptaszynski× Fumito, Masui× Rafal, Rzepka× Kenji, Araki |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this paper presents our research in automatic detection of emotionally loaded, or emotive sentences. We define the problem from a linguistic point of view assuming that emotive sentences stand out both lexically and grammatically. To verify this assumption we prepare a text classification experiment. In the experiment we apply language combinatorics approach to automatically extract emotive patterns from training sentences. the applied approach allows automatic extraction of not only widely used unigrams (tokens), or n-grams, but also more sophisticated patterns with disjointed elements. The results of experiments are explained with the use of means such as standard Precision, Recall and balanced F-score. The algorithm also provides a refined list of most frequent sophisticated patterns typical for both emotive and non-emotive context. The method reached results comparable to the state of the art, while the fact that it is fully automatic makes it more efficient and language independent. | |||||
| 書誌情報 |
Linguistics and Literature Studies 巻 5, 号 1, p. 36-50, 発行日 2017-01 |
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| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.13189/lls.2017.050103 | |||||
| 権利 | ||||||
| 権利情報 | HRPUB | |||||
| 出版者 | ||||||
| 出版者 | HRPUB | |||||
| 著者版フラグ | ||||||
| 値 | publisher | |||||
| 出版タイプ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
