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機械学習を活用したテキストマイニング(2) : 仮説の発見と検証
https://stars.repo.nii.ac.jp/records/9016
https://stars.repo.nii.ac.jp/records/90167b2c1083-83fb-4b21-a418-3acb64e90510
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
竹岡志朗 (2.2 MB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2019-03-27 | |||||
| タイトル | ||||||
| タイトル | 機械学習を活用したテキストマイニング(2) : 仮説の発見と検証 | |||||
| 言語 | ja | |||||
| タイトル | ||||||
| タイトル | Text Mining Using Machine Learning(2) : Discovery and Verification of Hypotheses | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | jpn | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | テキストマイニング | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | 機械学習 | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | 分散表現 | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | fastText | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | クチコミ | |||||
| 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 | departmental bulletin paper | |||||
| 著者 |
竹岡, 志朗
× 竹岡, 志朗× TAKEOKA, Shiro |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In recent years, AI has gained social attention as there are reports of leap in image recognition contest ILSVRC, the appearance of AlphaGo, and applied cases in various industrial fields. Words related to AI such as machine learning and deep learning has become widely recognized. As an application possibility to management research and management practice, it can be said that it is becoming a sufficiently usable technology due to improvement of natural language processing technology in recent years, especially improvement of distributed representation technology. In this research, we propose a method to analyze and visualize the characteristics of goods and services with text mining which using machine learning technology included in AI technologies, especially the method on discovery and verification of hypotheses. The text mining method proposed in this research is not based on current weighing text analysis which aggregate values of words, but based on distributed representation of words calculated by machine learning. By using this method, it is possible to analyze based on the consumer’s experience and meaning made up of the consuming process. The method proposed by this paper is also useful for business researchers, but even for practitioners, when planning and model change of new products or services, comparison with other company’s product services becomes easier than ever, more detailed analysis It is thought that it is beneficial because it can be considered to be able to proceed with practical work based on it. Text mining based on distributed representation is still an incomplete technology, not a fixed method, but it is an area where further development is expected in the future. |
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| 書誌情報 |
ja : 桃山学院大学経済経営論集 en : ST.ANDREW'S UNIVERSITY ECONOMIC AND BUSINESS REVIEW 巻 60, 号 4, p. 121-143, 発行日 2019-02-15 |
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| 出版者 | ||||||
| 出版者 | 桃山学院大学総合研究所 | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 02869721 | |||||
| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AN00240555 | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
