SNS 해시태그를 이용한 사용자 감정 분류 방법에 관한 연구

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Recently, studies are being actively carried out for sentiment analysis, which is a type of natural language processing technologies for analyzing subjective data such as opinions, attitudes, and propensities of users expressed on Web, blogs, and social network services (SNSs). Conventionally, to classify the sentiments of texts, most studies were carried out in a form of determining positive/negative/neutral sentiment by assigning polarity values for sentiment vocabulary by using a sentiment Lexicon. However, in this study, the sentiments were classified on the basis of Thayer's model defined psychologically, unlike the polarity classification used in the opinion mining.

In this paper, as a method of classifying the sentiments, sentiment categories were proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying this to user posts, sentiments can be determined through similarity measurement between the sentiment adjective candidates and sentiment category's sentiment keywords. Also, since sentiment distribution and major sentiments of users can be determined, the ambiguity of subject sentiments can be solved objectively. As a test result for the proposed method, the average accuracy rate for the whole sentiment categories was 90.7%, showing a good performance.

In this study, the sentiment categories were proposed by selecting happy, angry, peaceful, and sad as categories as typical sentiments to improve the accuracy of sentiment classification and minimize the misclassification rate; but in the future, if classifiable sentiments are additionally selected and expended on top of the four sentiments, it is thought that user sentiments can be analyzed more specifically. Furthermore, this study was carried out only with sentiment adjectives extracted by using texts, but it should be extended to a study including a method of using emoticons or other parts of speech containing sentiments.

Through the proposed method, if a sentiment classification system of large capacity is prepared, it is expected that sentiment analysis will be possible for various fields such as major issues and social phenomena through SNS, and furthermore, it is expected to be used in user tailored services, recommendation services, or sentiment marketing on SNS.
Alternative Title
A Study on Classification Method of User Sentiment using SNS Hashtags
Alternative Author(s)
Nam, Min Ji
산업기술융합대학원 소프트웨어융합공학과
Awarded Date
2015. 8
Table Of Contents

Ⅰ. 서론 1
A. 연구 배경 및 목적 1
B. 연구 내용 및 구성 2

Ⅱ. 관련 연구 3
A. 감정 분석(Sentiment Analysis) 3
1. 감정 정보 4
2. 소셜 네트워크 서비스(SNS) 데이터 기반 연구 5
B. 해시태그(Hashtag) 9
C. 감정 분류(Sentiment Classification) 12
1. 심리학적 감정 분류 12
2. 감정어 극성 분류 15
a. Pointwise Mutual Information(PMI)을 이용한 방법 16
b. 감정 사전(Sentiment Lexicon)을 이용한 방법 17

Ⅲ. 인스타그램 기반 사용자 감정 분석 18
A. 시스템 구성도 18
B. 인스타그램 감정 분류 프로세스 19
1. 카테고리 선정 및 감정 형용사가 포함된 해시태그 데이터 수집 19
2. 해시태그 전처리 과정 22
3. 감정 키워드 리스트 추출 26
4. 해시태그 기반 감정 카테고리 선정 29
C. 인스타그램 사용자 감정 분석 프로세스 31
1. 사용자 게시글 추출 32
2. 게시글 전처리 과정 35
3. 감정 형용사 후보 추출 36
4. 유사도 측정을 통한 사용자 감정 분석 37
Ⅳ. 실험 및 결과 41
A. 데이터 수집 41
B. 데이터 셋 43
1. 학습 데이터 셋 43
2. 실험 데이터 셋 44
C. 실험 평가 방법 및 결과 분석 45
1. 실험 평가 방법 45
2. 실험 결과 분석 45

Ⅴ. 결론 및 제언 48

참고문헌 49
남민지. (2015). SNS 해시태그를 이용한 사용자 감정 분류 방법에 관한 연구
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Engineering > Theses(Master)(산업기술창업대학원)
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