빅 데이터를 이용한 제품디자인의 감성반응 분석 : 스마트폰을 대상으로 = Sentimental analysis to product design using Big-Data : focused on smartphone

다국어 초록 (Multilingual Abstract)

Recently, research has been performed in various areas such as public administration, marketing, medical area, IT industry, and manufacturing area to analyze and apply big data. This study uses big data to structurally analyze the sentimental response of consumers on product design. Engineered analysis system using N-gram analysis and TF-IDF algorithm was developed to identify the possibility with alternative method about limitation of survey method used in sentimental analysis of general design, and the non-cognitive situations were acquired and analyzed.

Big data created from Twitter based on smart phones was collected to analyze by separating into preprocessing, processing, and postprocessing. Preprocessing is the stage of removing span and useless words in the collected data. Processing was classified into 14 categories including price, function, design, psychology, usability, advertisement, location, type comparison, prediction, period, brand, product name, purchase, and others to the consumer response about the products through pre- and post- investigation by applying the weighted value after extracting the key words in the text data by applying N-gram analysis and TF-IDF System.

The classified categories were performed with sentimental analysis, active analysis, and design response analysis. For sentimental analysis, 71 words were extract by using the 5 categories including psychology, design, function, price, and purchase by applying the opinion mining method. A chart was composed according to the frequency of word appearance. polar analysis was performed into positive, negative, and neutral on the extracted words.

For design response analysis, the response on the products were classified into function, usability, maintenance, economic, psychology, social, sensual, and environmental areas. Details were used to extract the factors with influence in the design response. The post-processing used wordcloud to effectively deliver the keyword, sentimental analysis, and result of polar analysis to the users. Then, these results were visualized, and factorial analysis, regression analysis, and statistical processing were executed on the 11 categories excluding the 3 categories including location, brand, and product name. As a result of the factor analysis, the main components including life photo function in comparison with iPhone 6S and 6S+ were extracted. In relations to the purchase opinion, significant influence was identified in the usability, purchase opinion, and psychological properties in relations to the new functions of iPhone.

As time passes to the response on the product, data was regularly collected to check the change of the main contents through tracking analysis. When comparing October analysis result, the new products showed high factors related to new function, price, and purchase according to release of product. For November, psychological response and various public opinions related to the review, price, and new function were identified. Through the polar analysis, the accumulated data was collected to provide response comparison of before and after the product. Comparison analysis of sentimental value on leading to brand loyalty is also possible. Also, this thesis paper can be used for feedback data and consumer response prediction through the change of public opinion, and it can be used as data for market analysis.

목차 (Table of Contents)

• 1. 서론 1
• 1.1 연구의 배경 및 문제제기 1
• 1.2 연구의 필요성 3
• 1.3 연구의 목표 5
• 1.4 연구의 내용 및 방법 6
• 2. 이론적 배경 9
• 2.1 감성의 개념정의 9
• 2.1.1 정서(affect) 9
• 2.1.2 감정(feeling) 12
• 2.1.3 감성(emotion) 14
• 2.2 커뮤니케이션 이론 18
• 2.2.1 쉐넌과 위버의 커뮤니케이션 모델 19
• 2.2.2 야콥슨의 커뮤니케이션 모델 20
• 2.3 감성분석방법 23
• 2.3.1 제품을 통한 감성교류 23
• 2.3.2 제품구매의 영향요인 25
• 2.3.3 소비자의 감성제품 구매행태 28
• 2.3.3.1 세대별 라이프스타일 특징 28
• 2.3.3.2 감성제품과 소비자의 인터랙션 35
• 2.3.4 감성품질의 구분 43
• 2.3.5 감성의 측정과 분석방법 45
• 2.4 빅 데이터(big data) 52
• 2.4.1 빅 데이터의 정의 52
• 2.4.2 빅 데이터 관련연구 54
• 2.4.3 빅 데이터 활용분야 58
• 2.4.4 빅 데이터를 활용한 소비자 감성반응 분석 59
• 2.4.5 텍스트 마이닝과 오피니언 마이닝 62
• 2.5 N-gram 분석 65
• 2.6 TF-IDF 알고리즘 67
• 3. 감성기반 비정형 빅 데이터 평가법 69
• 3.1 평가대상의 선정 69
• 3.2 분석프로세스 71
• 3.2.1 전처리 72
• 3.2.2 본처리 76
• 3.2.3 후처리 81
• 4. 평가결과 분석 83
• 4.1 형태소분석과 N-gram분석 83
• 4.2 데이터 분석 84
• 4.3 감성분석과 극성분석 112
• 4.4 패턴분석과 품사태깅 115
• 4.5 데이터시각화 116
• 4.6 디자인 반응분석 118
• 4.7 시간변화에 따른 궤적분석 124
• 5. 결론 및 제언 128
• 참고문헌 131
• 영문초록 137