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Sentiment root cause analysis based on relations among sentiment words

Sentiment root cause analysis based on relations among sentiment words

Material type
학위논문
Personal Author
박상민
Title Statement
Sentiment root cause analysis based on relations among sentiment words / Sang-min Park
Publication, Distribution, etc
Seoul :   Graduate School, Korea University,   2016  
Physical Medium
vii, 92장 : 도표 ; 26 cm
기타형태 저록
Sentiment root cause analysis based on relations among sentiment words   (DCOLL211009)000000068500  
학위논문주기
학위논문(박사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2016. 8
학과코드
0510   6YD36   310  
General Note
지도교수: 백두권  
Bibliography, Etc. Note
참고문헌: 장 78-90
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
Root Cause Analysis , Sentiment Analysis,,
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008 160630s2016 ulkd bmAC 000c eng
040 ▼a 211009 ▼c 211009 ▼d 211009
041 0 ▼a eng ▼b kor
085 0 ▼a 0510 ▼2 KDCP
090 ▼a 0510 ▼b 6YD36 ▼c 310
100 1 ▼a 박상민
245 1 0 ▼a Sentiment root cause analysis based on relations among sentiment words / ▼d Sang-min Park
260 ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2016
300 ▼a vii, 92장 : ▼b 도표 ; ▼c 26 cm
500 ▼a 지도교수: 백두권
502 1 ▼a 학위논문(박사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2016. 8
504 ▼a 참고문헌: 장 78-90
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a Root Cause Analysis ▼a Sentiment Analysis
776 0 ▼t Sentiment root cause analysis based on relations among sentiment words ▼w (DCOLL211009)000000068500
900 1 0 ▼a Park, Sang-min, ▼e
900 1 0 ▼a 백두권, ▼e 지도교수
900 1 0 ▼a Baik, Doo-kwon, ▼e 지도교수
945 ▼a KLPA

Electronic Information

No. Title Service
1
Sentiment root cause analysis based on relations among sentiment words (51회 열람)
View PDF Abstract Table of Contents

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6YD36 310 Accession No. 123054359 Availability Available Due Date Make a Reservation Service B M

Contents information

Abstract

Precise user-preference analysis is needed to generate a user profile for intelligent personal assistant. Aspect-level sentiment analysis can extract user preferences from product reviews but cannot explain the reasons for the user preferences because existing sentiment analyses only retrieve the polarity of a product feature. Further, it cannot consider the influence of sentiment words. Neutral sentiment words are not also utilized in sentiment analysis because these words do not affect the polarity calculation of the product feature. We propose a novel method that can analyze the root cause based on relations among words to extract the sentiment root cause. We use the fuzzy formal concept analysis to extend the feature-level hierarchy. A fuzzy cognitive map of the relations is employed to extract the root cause from the causes. The results show that we improved the accuracy of the sentiment cause and determination of the sentiment root-cause compared with the term frequency-based and sentiment score analyses.

Table of Contents

ABSTRACT	i
1. Introduction	1
1.1 Research Motivation	1
1.2 Research Purpose	2
1.3 Research Taxonomy	6
1.4 Organization of the Dissertation	9
2. Related Work	10
2.1 User-Preference Analysis	10
2.2 Fuzzy Theory	11
2.3 Sentiment Analysis	14
2.4 Ontology-based Analysis	16
2.5 Root-Cause Analysis	17
3. User-Preference Analysis	19
3.1 Device-Oriented User Preference Analysis	19
3.1.1 Problem Statement	20
3.1.2 User-Centric Product Recommendation	24
3.2 User-Oriented User-Preference Analysis	25
3.2.1 Problem Statement	25
3.2.2 Personal Ontology-based Sentiment Analysis	30
3.3 Content-Oriented User-Preference Analysis	31
3.3.1 Problem Statement	31
3.3.2 Multimodal Generative Story Graph Analysis	32
4. Sentiment Root-Cause Analysis	34
4.1 Architecture	36
4.2 Sentiment Ontology based on FFCA	38
4.2.1 Factual and Sentiment Ontologies	38
4.2.2 Hierarchical Tree Generation using FFCA	41
4.3 Sentiment-Cause Ontology based on FCM-R	44
4.3.1 Sentiment-Cause Ontology	44
4.3.2 Sentiment Root-Cause Extraction	47
4.4 Implementation	52
5. Experimental Evaluation	55
5.1 Experiment Dataset	55
5.2 Sentiment-Cause Ontology	57
5.3 Quantitative Evaluation	65
5.3.1 Sentiment-Cause Analysis	65
5.3.2 Sentiment Root-Cause Analysis with Frequency	66
5.3.3 Sentiment Root-Cause Analysis with Sentiment Score	67
5.4 Qualitative Evaluation	68
5.5 Discussion	72
6. Conclusion and Future Works	76
6.1 Conclusion	76
6.2 Future Works	77
Bibliography	78
Abstract (Korean)	91

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