What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Automatic Summarization in Chinese Product Reviews

With the increasing number of online comments, it was hard for buyers to find useful information in a short time so it made sense to do research on automatic summarization which fundamental work was focused on product reviews mining. Previous studies mainly focused on explicit features extraction whereas often ignored implicit features which hadn't been stated clearly but containing necessary information for analyzing comments. So how to quickly and accurately mine features from web reviews had important significance for summarization technology. In this paper, explicit features and “feature-opinion” pairs in the explicit sentences were extracted by Conditional Random Field and implicit product features were recognized by a bipartite graph model based on random walk algorithm. Then incorporating features and corresponding opinions into a structured text and the abstract was generated based on the extraction results. The experiment results demonstrated the proposed methods outpreferred baselines

Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"

Affected by urbanization, centralization and the decrease of overall population, Japan has been making efforts to revitalize the rural areas across the country. One particular effort is to increase tourism to these rural areas via regional branding, using local farm products as tourist attractions across Japan. Particularly, a program subsidized by the government called Michinoeki, which stands for 'roadside station', was created 20 years ago and it strives to provide a safe and comfortable space for cultural interaction between road travelers and the local community, as well as offering refreshment, and relevant information to travelers. However, despite its importance in the revitalization of the Japanese economy, studies with newer technologies and methodologies are lacking. Using sales data from establishments in the Kyushu area of Japan, we used Support Vector to classify content from Twitter into relevant topics and studied their causal relationship to the sales for each establishment using LiNGAM, a linear non-gaussian acyclic model built for causal structure analysis, to perform an improved market analysis considering more than just correlation. Under the hypotheses stated by the LiNGAM model, we discovered a positive causal relationship between the number of tweets mentioning those establishments, specially mentioning deserts, a need for better access and traf^ic options, and a potentially untapped customer base in motorcycle biker groups

Personalized Recommendation Model: An Online Comment Sentiment Based Analysis

Traditional recommendation algorithms measure users’ online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Users’ reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the fine-grained analysis of emotions expressed in users’ online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts feature-sentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user feature-sentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy

A case study on smart band

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 윤명환 .The aim of this study is to prove that the consumer review-based text mining methods proposed in the paper for cross-cultural design are effective. To prove it, we took Mi band 3 as a case study where we compared the cross-cultural differences in product preference of users from different cultural regions with this method. With the development of global market, more and more products and services are sold across the globe. Users from different cultures have different behaviors, cognitive styles, and value systems. Therefore, product should be designed to meet the needs and preferences of users from different cultural groups. In the field of cross-cultural design, existing studies are mainly focused on traditional usability and UX research methods. However, these methods expose some disadvantages when applied into cross-cultural design contexts. E-commerce websites provide a large volume of product reviews and it is easy to collect review data online. There is no need to employ foreign participants or make a survey onsite or remotely, which will save much more cost and time. There is a new trend that customer reviews are examined to know consumer opinions. Neverlessness, there are not many studies by analyzing online reviews in the field of cross-cultural design. Thus, my research proposed consumer review-based text mining methods for cross-cultural design, which consist of aspect-level opinion mining, sentiment analysis, and semantic network analysis. We collected review data from the following three websites: Naver of South Korea, Jingdong of China, and Amazon of the United States. Text mining methods including opinion mining, sentiment analysis, and semantic network analysis were performed. Firstly, product aspects were extracted from reviews according to word frequency. This indicates how much users are paying attention to different aspects of the product. Aspect-level sentiment analysis was conducted to find out customer satisfaction with different product aspects. Then, the words most associated with each product aspect were listed. Cluster analysis was conducted and the topic of each cluster was summarized. Data visualization of each dataset was done. Lastly, cross-cultural difference among three countries from the results was observed and discussed. Though there exist similar issues in product preferences of users from South Korea, China, and the United States, cross-cultural differences about Mi band 3 are shown in many product aspects. Korean tend to take Mi band as a fashionable, cool, yet not useful wearable device. They often buy it as a nice gift. They are interested in the appearance of the strap and often buy straps of different colors and materials. Korean do not enjoy outdoor activities as much as American. And the function of NFC is not prevalent in Korea. Thus, the smart band is not useful to Korean. These can explain why Korean do not care about quality of the smart band and do not want to buy Mi band at a high price. Korean think that the language of Korean on the display, application, and manual is the most important feature. The length of Korean texts is longer than Chinese to convey the same information. On the other hand, Korean prefer to check message notification on smart band rather than call notification. Therefore, Korean need a larger size for screen. Chinese are more concerned about different kinds of functions including fitness tracker (step counting, heart rate monitoring, and sleep monitoring), notification, and NFC. These different functions are all important and practical to Chinese. American enjoy outdoor activities and tend to use smart band mostly as activity tracker. They care more about activity tracker function including heart rate monitoring and step counting than Korean and Chinese. They have a higher requirement about the accuracy of measured data and have more negative reviews on activity tracker function than Korean and Chinese. Besides, they need the mode for swimming. Because American usually use the smart band for outdoor activities, they complain a lot that the screen is prone to scratches and is invisible under the outdoor sunlight. Also, they pay attention to the quality of screen and strap, expecting the material make the screen and strap durable. Besides, battery is the most significant aspect to American. They always try to test each function to find which function makes battery life short. The results of the case study prove that the consumer review-based text mining method proposed in the paper can generate cross-cultural difference in product preference effectively, which is helpful to cross-cultural design research. And this method is relatively easy and fast compared to other conventional methods.Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Research Objective 3 1.3 Organization of the Thesis 4 Chapter 2. Literature Review 5 2.1 Cross-Cultural Design 5 2.1.1 Definition 5 2.1.2 Necessity 6 2.1.3 Method 7 2.2 Opinion Mining and Sentiment Analysis 10 2.2.1 Aspect Level Opinion Mining 10 2.2.2 Cross-Lingual Opinion Mining 11 2.3 Semantic Network Analysis 13 Chapter 3. Methodology 15 3.1 Data Collection 15 3.2 Data Processing 16 3.2.1 Text Preprocessing 16 3.2.2 Opinion Mining and Sentiment Analysis 16 3.2.3 Semantic Network Analysis 17 3.2.4 Result Sample 18 Chapter 4. Result 20 4.1 Overview 20 4.2 Opinion Mining and Sentiment Analysis 21 4.2.1 Normalized Frequency 21 4.2.2 Sentiment Analysis 23 4.3 Semantic Network Analysis 26 4.3.1 Associated Words 26 4.3.1 Cluster Analysis 31 4.3.1 Data Visualization 34 4.4 Results based on Aspects 37 4.4.1 Battery 37 4.4.2 Price 39 4.4.3 Function 41 4.4.4 Step Counting 43 4.4.5 Korean 45 4.4.6 Heart Rate Monitoring 47 4.4.7 Sleep Monitoring 49 4.4.8 Quality 51 4.4.9 Notification 53 4.4.10 Screen 55 4.4.11 Exercise 57 4.4.12 App 59 4.4.13 Call 61 4.4.14 Connection 63 4.4.15 Waterproof 65 4.4.16 Display 67 4.4.17 Message 69 4.4.18 Alarm 71 4.4.19 Gift 73 4.4.20 Strap 75 Chapter 5. Conclusion 78 5.1 Summary of Findings 78 5.2 Future Research 80 Bibliography 82Maste