Measurement and quantification of an individual's feelings for a place in personal data analysis

Seiji Kasuya, Kiichi Tago, Qun Jin*

*この研究の対応する著者

研究成果: Article査読

抄録

Location-related data are an important subset of personal data. An individual may have a positive or negative feeling for a specific place, which is important for personal data analysis. There are many studies on sentiment analysis within text data, such as tweets, but few studies have been conducted specifically on an individual's feelings regarding locations. In this study, we focus on measuring and quantifying an individual's feelings for a place using three representative methods in sentiment analysis: emotion dictionary, personalized dictionary, and Bayesian classification. We design an experiment to evaluate these methods using tweet data including locations and an individual's emotional changes with regard to these locations before entering, after exiting, and in a location. Three sets of emotion scores are obtained and normalized. Furthermore, we set four protocols and use statistical methods to compare these emotion scores with the subjective emotion scores provided by the user whose tweets are used in the experiment. Experimental results show that Bayesian classification performs the best in measuring and quantifying an individual's feelings for a place.

本文言語English
ページ(範囲)739-749
ページ数11
ジャーナルHuman Behavior and Emerging Technologies
3
5
DOI
出版ステータスPublished - 2021 12月

ASJC Scopus subject areas

  • 社会心理学
  • 社会科学(全般)
  • 人間とコンピュータの相互作用

フィンガープリント

「Measurement and quantification of an individual's feelings for a place in personal data analysis」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル