Analyzing influence of emotional tweets on user relationships using Naive Bayes and dependency parsing

Kiichi Tago, Kosuke Takagi, Seiji Kasuya, Qun Jin

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    Twitter is one of the most popular social network services (SNS) applications, in which users can casually post their messages. Given that users can easily post what they feel, Twitter is widely used as a platform to express emotions. These emotional expressions are considered to possibly influence user relationships on Twitter. In our previous study, we analyzed this influence using emotional word dictionaries. However, we could not measure the emotion scores for the words not included in the dictionaries. To solve this problem, in this study, we use the Naive Bayes and consider dependency parsing, i.e., the structure of tweets and the relationships of words. Furthermore, we introduce a set of new measures, namely total positive emotion score (TPES), total negative emotion score (TNES), and total neutral emotion score (TNtES). Based on these measures, we define a new composite index (CI) for emotion scores, which is a normalized value in the range of 0 to 1. We categorize users into positive and negative groups based on the composite index and test the difference of user relationships between these two groups with a statistical method. The result demonstrates that the relationships of positive users not only get better (i.e., the number increases) with time, but also tends to be mutual, which is consistent with the result of our previous study.

    Original languageEnglish
    Pages (from-to)1-16
    Number of pages16
    JournalWorld Wide Web
    DOIs
    Publication statusAccepted/In press - 2018 May 25

    Fingerprint

    Glossaries
    Composite materials
    Statistical methods

    Keywords

    • Brunner–Munzel test
    • Dependency parsing
    • Emotional tweets
    • Influence analysis
    • Naive Bayes

    ASJC Scopus subject areas

    • Software
    • Hardware and Architecture
    • Computer Networks and Communications

    Cite this

    Analyzing influence of emotional tweets on user relationships using Naive Bayes and dependency parsing. / Tago, Kiichi; Takagi, Kosuke; Kasuya, Seiji; Jin, Qun.

    In: World Wide Web, 25.05.2018, p. 1-16.

    Research output: Contribution to journalArticle

    @article{4edceff21cab4af099b3712a116e005d,
    title = "Analyzing influence of emotional tweets on user relationships using Naive Bayes and dependency parsing",
    abstract = "Twitter is one of the most popular social network services (SNS) applications, in which users can casually post their messages. Given that users can easily post what they feel, Twitter is widely used as a platform to express emotions. These emotional expressions are considered to possibly influence user relationships on Twitter. In our previous study, we analyzed this influence using emotional word dictionaries. However, we could not measure the emotion scores for the words not included in the dictionaries. To solve this problem, in this study, we use the Naive Bayes and consider dependency parsing, i.e., the structure of tweets and the relationships of words. Furthermore, we introduce a set of new measures, namely total positive emotion score (TPES), total negative emotion score (TNES), and total neutral emotion score (TNtES). Based on these measures, we define a new composite index (CI) for emotion scores, which is a normalized value in the range of 0 to 1. We categorize users into positive and negative groups based on the composite index and test the difference of user relationships between these two groups with a statistical method. The result demonstrates that the relationships of positive users not only get better (i.e., the number increases) with time, but also tends to be mutual, which is consistent with the result of our previous study.",
    keywords = "Brunner–Munzel test, Dependency parsing, Emotional tweets, Influence analysis, Naive Bayes",
    author = "Kiichi Tago and Kosuke Takagi and Seiji Kasuya and Qun Jin",
    year = "2018",
    month = "5",
    day = "25",
    doi = "10.1007/s11280-018-0587-9",
    language = "English",
    pages = "1--16",
    journal = "World Wide Web",
    issn = "1386-145X",
    publisher = "Springer New York",

    }

    TY - JOUR

    T1 - Analyzing influence of emotional tweets on user relationships using Naive Bayes and dependency parsing

    AU - Tago, Kiichi

    AU - Takagi, Kosuke

    AU - Kasuya, Seiji

    AU - Jin, Qun

    PY - 2018/5/25

    Y1 - 2018/5/25

    N2 - Twitter is one of the most popular social network services (SNS) applications, in which users can casually post their messages. Given that users can easily post what they feel, Twitter is widely used as a platform to express emotions. These emotional expressions are considered to possibly influence user relationships on Twitter. In our previous study, we analyzed this influence using emotional word dictionaries. However, we could not measure the emotion scores for the words not included in the dictionaries. To solve this problem, in this study, we use the Naive Bayes and consider dependency parsing, i.e., the structure of tweets and the relationships of words. Furthermore, we introduce a set of new measures, namely total positive emotion score (TPES), total negative emotion score (TNES), and total neutral emotion score (TNtES). Based on these measures, we define a new composite index (CI) for emotion scores, which is a normalized value in the range of 0 to 1. We categorize users into positive and negative groups based on the composite index and test the difference of user relationships between these two groups with a statistical method. The result demonstrates that the relationships of positive users not only get better (i.e., the number increases) with time, but also tends to be mutual, which is consistent with the result of our previous study.

    AB - Twitter is one of the most popular social network services (SNS) applications, in which users can casually post their messages. Given that users can easily post what they feel, Twitter is widely used as a platform to express emotions. These emotional expressions are considered to possibly influence user relationships on Twitter. In our previous study, we analyzed this influence using emotional word dictionaries. However, we could not measure the emotion scores for the words not included in the dictionaries. To solve this problem, in this study, we use the Naive Bayes and consider dependency parsing, i.e., the structure of tweets and the relationships of words. Furthermore, we introduce a set of new measures, namely total positive emotion score (TPES), total negative emotion score (TNES), and total neutral emotion score (TNtES). Based on these measures, we define a new composite index (CI) for emotion scores, which is a normalized value in the range of 0 to 1. We categorize users into positive and negative groups based on the composite index and test the difference of user relationships between these two groups with a statistical method. The result demonstrates that the relationships of positive users not only get better (i.e., the number increases) with time, but also tends to be mutual, which is consistent with the result of our previous study.

    KW - Brunner–Munzel test

    KW - Dependency parsing

    KW - Emotional tweets

    KW - Influence analysis

    KW - Naive Bayes

    UR - http://www.scopus.com/inward/record.url?scp=85047442784&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85047442784&partnerID=8YFLogxK

    U2 - 10.1007/s11280-018-0587-9

    DO - 10.1007/s11280-018-0587-9

    M3 - Article

    AN - SCOPUS:85047442784

    SP - 1

    EP - 16

    JO - World Wide Web

    JF - World Wide Web

    SN - 1386-145X

    ER -