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

Saki Kitaoka, Takashi Hasuike

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1-8
    Number of pages8
    Volume2018-January
    ISBN (Electronic)9781538627259
    DOIs
    Publication statusPublished - 2018 Feb 2
    Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
    Duration: 2017 Nov 272017 Dec 1

    Other

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

    Keywords

    • location analysis
    • sentiment analysis
    • social media
    • Twitter

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Control and Optimization

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  • Cite this

    Kitaoka, S., & Hasuike, T. (2018). Where is safe: Analyzing the relationship between the area and emotion using Twitter data. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (Vol. 2018-January, pp. 1-8). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8285210