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감성분석과 Word2vec을 이용한 비정형 품질 데이터 분석

Title
감성분석과 Word2vec을 이용한 비정형 품질 데이터 분석
Other Titles
Informal Quality Data Analysis via Sentimental analysis and Word2vec method
Author
배석주
Keywords
Naive Bayes; Random Forest; Sentimental Analysis; Support Vector Machine; Text Mining; Word2vec
Issue Date
2017-03
Publisher
한국품질경영학회
Citation
품질경영학회지, v. 45, no. 1, page. 117-128
Abstract
Purpose: This study analyzes automobile quality review data to develop alternative analytical method of informal data. Existing methods to analyze informal data are based mainly on the frequency of informal data, however, this research tries to use correlation information of each informal data.Method: After sentimental analysis to acquire the user information for automobile products, three classification methods, that is, naïve Bayes, random forest, and support vector machine, were employed to accurately classify the informal user opinions with respect to automobile qualities. Additionally, Word2vec was applied to discover correlated information about informal data.Result: As applicative results of three classification methods, random forest method shows most effective results compared to the other classification methods. Word2vec method manages to discover closest relevant data with automobile components.Conclusion: The proposed method shows its effectiveness in terms of accuracy and sensitivity on the analysis of informal quality data, however, only two sentiments (positive or negative) can be categorized due to human errors. Further studies are required to derive more sentiments to accurately classify informal quality data.Word2vec method also shows comparative results to discover the relevance of components precisely.
URI
https://www.jksqm.org/journal/view.php?doi=10.7469/JKSQM.2017.45.1.117https://repository.hanyang.ac.kr/handle/20.500.11754/112996
ISSN
1229-1889; 2287-9005
DOI
10.7469/JKSQM.2017.45.1.117
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INDUSTRIAL ENGINEERING(산업공학과) > Articles
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