Hindi Sentiment Analysis

With the evolution of web technology, a huge amount of data is present on the web. In addition to exploring the resources present on web, the users also provide feedback thus generating additional useful data. Thus mining of data and identifying user sentiments is the need of the hour. Sentiment analysis is the natural language processing task that mines information from various text forms such as blogs, reviews and classify them on the basis of polarity such as positive, negative or neutral. Hindi is the national language of India and is spoken by 366 million people across the world. The percentage of web content in Hindi is growing at lightning speed. A lot of research in opinion mining is carried out in English language but there are not many instances of research done in Hindi language. In this paper we have proposed a strategy for classifying given Hindi texts in to different classes and then extract sentiments in terms of positive, negative and neutral for identified classes. Naive Bayes, Modified Maximum entropy are used for classification and HindiSentiWordNet (HSWN) is used to determine the polarity of individual class

BERT, SHAP, Kano 모델을 기반한 소비자 만족 요소 다이나믹스

학위논문(석사) -- 서울대학교대학원 : 경영대학 경영학과, 2022.2. 오정석 교수.최근 10년 간 온라인 쇼핑 산업의 성장으로 온라인 쇼핑몰 플랫폼에 온라인 리뷰 등 무한한 소비자 반응, 만족도 관련 정보가 생성되고 있다. 이에 많은 기업들과 학계에서 이를 활용하여 VoC (Voice of Customer)를 반영한 소비자 만족도 모델링을 시도하고 있다. 본 논문은 BERT, GBM, SHAP 등을 활용하여 카노 모델 (Kano Model)에 기반한 소비자 만족도 특성 (Customer Satisfaction Dimension)을 분류하고 각 특성의 소비자 요구 충족 여부가 소비자 만족도에 미치는 영향도를 측정한다. 본 논문의 방법론에 활용된 각 빅데이터 모델 성능과 선행 연구들에서 사용된 모델 성능을 직접 구현 및 비교하여, 본 논문에서 활용된 모델들의 정확성과 안정성을 보였다. 또한 해석적 머신러닝 기법인 SHAP를 도입하여, 카노 카테고리를 분류하는 통일된 분류 방식을 제안한다. 본 연구는 제시된 방법론을 통해 스마트폰 및 스마트워치 제품군을 대상으로 실증 연구를 진행하며, 산업계에 제품 개발 및 개선, 고객 세분화 전략 등 기업 의사결정 방향성에 유의미한 제언을 제시함으로써 본 방법론의 실용적 가치를 입증하였다.As a large number of online reviews are loaded on e-commerce platforms in recent days, companies are being able to measure customer satisfaction reflecting VoC (Voice of Customer) with big data analytics. This paper proposes the improved framework for identifying characteristics of customer satisfaction dimensions (CSD) based on Kano model using BERT (Bidirectional Encoder Representations from Transformers), GBM (Gradient Boosting Machine), and SHAP (Shapley Additive eXplanation). We proved each model outperformance by comparing other models which previous studies have used. And this paper suggests the unified rule of Kano model classification using SHAP. Furthermore, we conducted empirical studies regarding smartphone and smartwatch products which suggest the direction of product enhancement/development strategy and multi-product level customer segmentation strategy to product manufacturers. This shows proposed methodology’s effectiveness and usefulness on industrial analysis.1. Introduction 1 2. A framework for modelling customer satisfaction from online review 5 3. Research Method 8 3.1 Mining customer’s sentiments toward CSDs from online reviews 8 3.2 Measuring the effects of customer sentiments toward each CSD on customer satisfaction 11 3.3 Identifying the feature of each CSD from the customer’s perspective 11 3.4 Classifying each CSD into Kano categories 14 4. Empirical Study 17 4.1 Study 1 17 4.2 Study 2 24 5. Conclusion 27 6. Reference 29석

The Impact of Consumer Perceptions of Tanking on National Basketball Association Attendance

This dissertation studies the impact of consumer perceptions of tanking on National Basketball Attendance (NBA) attendance. The prevalence of tanking in the NBA raised concerns that some teams were purposely avoiding winning games in order to improve their draft position. The majority of previous studies on tanking have focused on developing empirical evidence of the existence of tanking in sport. Yet, no study systematically explored the impact of perceived tanking behavior on consumer demand for sport. As tanking teams rarely reveal their tanking strategy to the public, fans may not correctly identify tanking behavior in sport, and thus are likely to rely on their perceptions of tanking to make attendance decisions. The current dissertation employs tanking discussions on the social media platform Twitter along with data mining tools to quantify consumer perceptions of tanking. Econometric models are then utilized to analyze the effect of the perceived tanking behavior on demand for NBA games. The estimation results provide robust evidence that the increasing awareness of tanking for home teams hurts NBA attendance in both the short and long term. This dissertation also reveals that more negative attitudes toward visiting teams’ tanking behavior can undermine consumer interest in attending NBA games. These findings offer important managerial implications on the urgency of restraining tanking behavior as well as the importance of maintaining integrity in sports competitions