Analyzing Influence of Emotional Tweets on User Relationships by Naive Bayes Classification and Statistical Tests

Kiichi Tago, Qun Jin

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    As an SNS, Twitter is popular because users can post their emotions as a short message easily. Emotional tweets may influence user relationships. In our previous study, we found that positive users construct mutual relationships in Twitter. Keyword matching with emotional word dictionaries was used to detect positive users. The problem of keyword matching is the limitation of word number. To solve this problem, we use machine learning, specifically Naive Bayes Classification, to classify emotions of tweets. We analyze whether there is a difference in user relationships between the positive and negative groups by the Brunner-Munzel test. The result shows that the relationships of positive users increase more than that of negative users in the followee fluctuation, follower fluctuation and mutual follow fluctuation, which means that a positive user is more active to construct user relationships than a negative user.

    本文言語English
    ホスト出版物のタイトルProceedings - 2017 IEEE 10th International Conference on Service-Oriented Computing and Applications, SOCA 2017
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ217-222
    ページ数6
    2017-January
    ISBN(電子版)9781538613269
    DOI
    出版ステータスPublished - 2017 12 28
    イベント10th IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2017 - Kanazawa, Japan
    継続期間: 2017 11 222017 11 25

    Other

    Other10th IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2017
    国/地域Japan
    CityKanazawa
    Period17/11/2217/11/25

    ASJC Scopus subject areas

    • コンピュータ ネットワークおよび通信
    • コンピュータ サイエンスの応用
    • ハードウェアとアーキテクチャ
    • 情報システムおよび情報管理

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