Build Sentiment Classification Prediction Model for O2O Service Long-Sheng Chen Wan-Ting Chien Hua-Nan Chang Department of Information Department of Information Department of Golden-Ager Management Management Management 168, Jifeng E. Rd., Wufeng District, 168, Jifeng E. Rd., Wufeng District, 168, Jifeng E. Rd., Wufeng District, Taichung, 41349 Taiwan, R.O.C. Taichung, 41349 Taiwan, R.O.C Taichung, 41349 Taiwan, R.O.C 886-4-23323000 ext. 5001 886-4-23323000 ext. 4167 886-4-23323000 ext. 6001 [email protected] [email protected] [email protected] ABSTRACT restaurants has reached 10,000 from 2008 to 2016. Therefore, in With the rapid development of information and communication the recent rapid development of O2O, more and more enterprises technology, O2O (Online to Offline) business model has attracted need to face fierce competition to enter this market. It also lots of attentions for enterprises. In such a fast-growing become harder to attract customers in the O2O market. environment, some studies indicated that lack of trust will bring a In Taiwan, O2O platforms developed very rapid, but many great damage to O2O business. Besides, some published works problems occur. Take “meal vouchers” in group buying for pointed out those negative comments in social communities will example. There have been too many consumer disputes, including decrease the consumer's trust to O2O companies and platforms. the restaurant service information provided by platforms is So, it is necessary for enterprises to understand the important inconsistent to the actual stores, meal vouchers use information is factors that affect consumers’ sentiment of textual reviews. not detailed, or the meal coupon trading platform mixed with Therefore, this study aims to build prediction models by using frauds and so on. Support Vector Machines Recursive Feature Elimination (SVM- RFE) and Least Absolute Shrinkage and Selection Operator According to previously reported facts, before O2O users doing (LASSO), respectively. We do not only attempt to build sentiment purchases, they will first browse others’ reviews to understand the classification models, but also to find the important factors that authenticity of the platform information, integrity, credibility, and affect the sentiments of comments. The findings can be references other elements of trust. With these initial trust, online transactions for O2O market enterprises to carefully answer customers’ will be completed. Consequently, scholars believe that consumer comments to improve customers’ trust and service quality. trust will be an important factor in the success of O2O online transactions (Kim et al., 2005, Liang et al., 2014). Some studies CCS Concepts also confirmed that customer trust in O2O development is one of • Information systems➝ World Wide Web➝ Web important issues. Liang et al. (2014) indicated that consumer trust applications➝ Electronic commerce➝ Online shopping will affect the development of O2O (Liang et al., 2014). Wu et al. (2015) also believe that confusion and insufficient trust will bring Keywords a big problem for O2O (Wu et al., 2015). To sum up, we believe O2O; Sentiment classification; Feature selection; SVM-RFE; that customer trust will be a critical factor to O2O. So, it is very LASSO important to enhance the customer trust of O2O platforms and manufacturers. 1. INTRODUCTION O2O (Online to Offline or Offline to Online) refers to consumers Textual comments in social media could be considered as the can buy products/services from the network platform (online) and electronic word of mouth. These textual reviews in social then get purchased products or enjoy services in physical stores communities have become one of major references for consumers (offline). Many literatures pointed out that many companies in to make purchase decisions (Yan et al., 2016). According to China are greatly concerned about the development of O2O. They previous literatures, the reputations of companies and website compete to enter the O2O market because O2O has been platforms are part of customer trust. However, Sparks and considered to have a great potential of growth (Carsten et al., Browning (2011) indicated that electronic word of mouth 2016; Zhang and Huang, 2015). In the world, lots of O2O services contributes to the development of reputation and customer trust has been developed successfully, such as group buying (Groupon, (Sparks and Browning, 2011). Textual comments contain positive gomaji), food and beverage service (OPENTABLE, EzTable, and negative sentiments. Sentiment classification aims to classify Dianping and Yelp), transportation (Uber and Zipcar), travel textual comments into positive and negative sentiments. If we can service (Airbnb and TripAdvisor) and so on. The rise of O2O has know the sentiment of customers from textual comments, O2O led to a gradual change in consumer spending and payment, and service providers can know the acceptance levels regarding their has contributed to the development of the online cash flow provided products and services. Moreover, they can further industry (Zhang and Hung, 2015). According to the report of the improve the quality of the service or product and give consumers world's famous OpenTable restaurant booking website, it has appropriate responses (Prabowo and Thelwall, 2009; Pang et al., more than 1 billion orders from when it has been established to 2002). Hence, they can enhance customer trust of O2O business. 2016. And there are more than 40,000 restaurants have cooperated Besides, some published works pointed out those negative with OpenTable. In another well-known restaurant O2O website comments in social communities will decrease the consumer's in Taiwan, EzTable, the number of cooperated high-level trust to O2O companies and platforms. So, it is necessary for enterprises to understand the important factors that affect they can use the results of sentiment classification as a basis for consumers’ sentiment of textual reviews. decision making to decide whether to order services or products. In available O2O related literatures, this issue is often conducted There are many sentiment classification methods. One of them is using a questionnaire survey (Wu et al., 2015, Li et al., 2016). semantic orientation which is to set up a positive and negative And the way of questionnaire survey cannot provide immediate vocabulary, and then calculate the scores, depending on the customer thinking, and it requires a lot of manpower and time. relationship between a specific textual document and the Therefore, this study aims to build prediction models by using vocabulary, to determine the semantic orientation of the document. Support Vector Machines Recursive Feature Elimination (SVM- In addition, many literatures have successfully applied the RFE) and Least Absolute Shrinkage and Selection Operator semantic orientation methods to complete the sentiment (LASSO), respectively. We do not only attempt to build sentiment classification, such as Murthy and Suresha (2015) who used the classification models, but also to find the important factors that semantic orientation method to effectively classify XML pages affect the sentiments of comments. The findings can be references (Murthy and Suresha, 2015), Chaovalit and Zhou (2005) used the for O2O market enterprises to carefully answer customers’ semantic orientation (Chaovalit and Zhou, 2005), unsupervised comments to improve customers’ trust and service quality. and supervised learning methods to classify film reviews. Another kind of common methods of sentiment classification is 2. LITERATURE REVIEW machine learning. Machine learning methods can have higher 2.1 O2O (Online to Offline) prediction accuracy, but them needs to spending lots of time to Recently, many businessmen and researchers paid much attention define class labels and train model. If we do not use machine on O2O business. O2O can be considered as a seamless shopping learning model instead of SO methods, the positive and negative experience between online business and offline entities provided term sets need to be updated. And the number of vocabularies by any devices. There has been a need for more scholars to should be increased dramatically, when the number of reviews suggest how firms can provide a more attractive approach to the grows. So, this study uses SO methods to help defining class O2O model and maintain a competitive advantage (Tsaia et al., labels and then use machine learning methods to build sentiment 2015; Wu et al., 2015; He et al., 2016). classification model. However, under the rapid development of O2O, some scholars noticed that there may be a trust problem in the O2O environment 3. METHODLOGY (Wu et al., 2015; Liang et al., 2014). From the available literatures, There are several steps in this experiment. We need to define the customer trust issue in O2O is not sufficient. So, this study factors of influencing positive and negative sentiments, collect attempts to extend this topic for discussion data, establish training set, prepare data, implement feature selection by using SVM-RFE and LASSO, build predictive model At present, O2O related literatures are almost all use the by using SVM, assess selected important factors, and finally make questionnaire to do qualitative researches (Li et al., 2016; Wu et conclusions. The concise steps describes as below. al., 2015). It might have some problems. For examples, we cannot make sure whether the respondents really use O2O services. And Step1: define factors of influencing