Filtering Method for Twitter Streaming Data Using Human-in-the-Loop Machine Learning

• Item type: Trans(1)
• 公開日: 2019-04-11
• タイトル: Filtering Method for Twitter Streaming Data Using Human-in-the-Loop Machine Learning
• タイトル / 言語: en
• タイトル: Filtering Method for Twitter Streaming Data Using Human-in-the-Loop Machine Learning
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [研究論文] Information filtering, Human-in-the-loop, Human factor, Machine learning
• 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
• 著者所属: Nara Institute of Science and Technology
• 著者所属(英): en
• 著者所属(英): Nara Institute of Science and Technology
• 著者名: Yu, Suzuki
• 著者名(英): Yu, Suzuki
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: A large number of texts is posted daily on social media. However, only a small portion of these texts is informative for a specific purpose. For example, in order to collect a set of tweets for marketing strategy, we should collect a large number of tweets related to a specific topic with high accuracy. If we accurately filter the texts, we can continuously obtain fresh and useful information in real time. In a keyword-based approach, filters are constructed using keywords, but selecting the appropriate keywords is often tricky. In this work, we propose a method for filtering texts that are related to specific topics using a classification method that is based on crowdsourcing and machine learning. In our approach, we construct a text classifier using fastText and then annotate whether the tweets are related to the topics using crowdsourcing. For constructing an accurate classifier, we should prepare a large amount of learning data. However, this process is costly and time-consuming. To construct an accurate classifier using a small number of learning data, we consider two strategies for selecting tweets which the crowdsourcing participants should assess: optimistic and pessimistic approach. Then, we reconstruct the text classifier using the annotated texts and classify them again. If we continue instigating this loop, the accuracy of the classifier will improve, and we will obtain useful information without having to specify the keywords. Experimental results demonstrate that our proposed system is adequate for filtering social media streams. Moreover, we discovered that the pessimistic approach is better than the optimistic approach.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.27(2019) (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: A large number of texts is posted daily on social media. However, only a small portion of these texts is informative for a specific purpose. For example, in order to collect a set of tweets for marketing strategy, we should collect a large number of tweets related to a specific topic with high accuracy. If we accurately filter the texts, we can continuously obtain fresh and useful information in real time. In a keyword-based approach, filters are constructed using keywords, but selecting the appropriate keywords is often tricky. In this work, we propose a method for filtering texts that are related to specific topics using a classification method that is based on crowdsourcing and machine learning. In our approach, we construct a text classifier using fastText and then annotate whether the tweets are related to the topics using crowdsourcing. For constructing an accurate classifier, we should prepare a large amount of learning data. However, this process is costly and time-consuming. To construct an accurate classifier using a small number of learning data, we consider two strategies for selecting tweets which the crowdsourcing participants should assess: optimistic and pessimistic approach. Then, we reconstruct the text classifier using the annotated texts and classify them again. If we continue instigating this loop, the accuracy of the classifier will improve, and we will obtain useful information without having to specify the keywords. Experimental results demonstrate that our proposed system is adequate for filtering social media streams. Moreover, we discovered that the pessimistic approach is better than the optimistic approach.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.27(2019) (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464847
• 書誌情報: 情報処理学会論文誌データベース（TOD）; 巻 12, 号 2, 発行日 2019-04-11
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7799
• 出版者 / 言語: ja
• 出版者: 情報処理学会