Analyzing Spatial Characteristics of Stress Topics Using Tweet Data (트윗데이터를 활용한 스트레스 토픽의 지역별 특징 분석)

Aetti Kang (강애띠); Youngok Kang (강영옥)

doi:10.16879/jkca.2018.18.2.053

Analyzing Spatial Characteristics of Stress Topics Using Tweet Data

Journal of the Korean Cartographic Association
Abbr : JKCA
2018, 18(2), pp.53-69
DOI : 10.16879/jkca.2018.18.2.053
Publisher : The Korean Cartographic Association
Research Area : Social Science > Geography > Geography in general > Cartography
Published : August 31, 2018

Aetti Kang ¹, Youngok Kang ²

¹한국국토정보공사 공간정보연구원
²이화여자대학교

Accredited

ABSTRACT

This study aims how to find the spatial characteristics by region on the stress sentiment and the topics extracted from tweet data by twitter users. We downloaded 503,737 tweet data from July 12, 2014 to July 21, 2015, but analyzed 332,328 tweet data excluding advertisement and news. We classified tweet texts into morphemes in order to analyze the tweet data and applied the topic modeling based on LDA algorithm to search the topic about stress. As a result, 15 topics were generated and classified into 3 categories such as causes, results and relief methods of stress. The topics related to causes of stress are “personality”, “learning”, “job”, “family”, and “SNS use”, those related to results of stress are “illness”, “mental status”, “hair loss” and those related to relief methods of stress are “painting”, “gaming”, “exercise and cultural life”, “food intake”, “music”. Tweet data that home location are clearly identified are only 1,035 among 332,328 tweet data. So, we geolocated 34,641 tweet data through the residence model. We analyzed the results of the topic modeling by using 17 administrative districts (Si-Do) and then identified maximum and minimum number of topics. Finally, we compared which topics are highly interested in 17 administrative districts according to causes, results, and relief methods of stress. This paper is meaningful in two folds: First, this paper shows how to represent the meaning of people's sentiment which is the social pathology of stress. Second, this paper extends the field of spatial data by mapping invisible and emotional phenomena.

KEYWORDS

Text mining, Topic modeling, Tweet, Social Network Service, Stress, Spatial data analysis

Citation status

* References for papers published after 2023 are currently being built.

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Journal of the Korean Cartographic Association 2023 KCI Impact Factor : 1.34

Analyzing Spatial Characteristics of Stress Topics Using Tweet Data

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of the Korean Cartographic Association 2023 KCI Impact Factor : 1.34

Analyzing Spatial Characteristics of Stress Topics Using Tweet Data

ABSTRACT

KEYWORDS

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REFERENCES (33) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.