Where is safe: Analyzing the relationship between the area and emotion using Twitter data

Saki Kitaoka, Takashi Hasuike

    研究成果: Conference contribution

    4 被引用数 (Scopus)

    抄録

    Understanding the geographic and environmental factors that affect local criminal activity is important for crime prevention. In this study, we use data from Geo-Twitter to analyze the reasons underlying the occurrence of local criminal activity. In recent years, abundant location-based social network (LBSN) (e.g., Foursquare, Geo-Twitter) data has become easily available at a low cost. Therefore, many studies have used LBSNs data to model and understand human mobile behavior, such as patterns of human travel and activity. However, few studies on local criminal activities have been reported. In this paper, a new methodology is proposed to identify the reasons underlying local criminal activity from the view point of geographic and environmental factors. Our methodology consists of the following steps. First, we collect geo-tagged data from Twitter. In particular, we extract a large corpus with geo-tags, called tweets, from major cities in the United States. Second, we measure the sentiments expressed in tweets posted from a specific area using the fastText model [1]. Third, we apply a simple clustering technique called latent Dirichlet allocation (LDA) to identify the topics that are clustered in each area using sentimental analysis. Lastly, we analyze the reasons for crime by comparing the topics and the information on all crime using the data portal.

    本文言語English
    ホスト出版物のタイトル2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ1-8
    ページ数8
    2018-January
    ISBN(電子版)9781538627259
    DOI
    出版ステータスPublished - 2018 2月 2
    イベント2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
    継続期間: 2017 11月 272017 12月 1

    Other

    Other2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
    国/地域United States
    CityHonolulu
    Period17/11/2717/12/1

    ASJC Scopus subject areas

    • 人工知能
    • コンピュータ サイエンスの応用
    • 制御と最適化

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