What Made You Success: Text Mining Analysis of Trip Advisor Traveler Choice Hotel

Due to the availability of the reviews in review websites, customers have plenty of choices to proceed with booking. Hospitality industry introduced many innovative technologies to gain customer satisfaction and to do the easy of business. This paper tries to address the factors made to achieve customer loyalty and what made to win the Trip Advisor travelers choice award. We did the text mining analysis of the satisfied and dissatisfied customer reviews of the hotel. We performed frequency analysis, polarity analysis, sentiment analysis, and cluster analysis. The study finds that the positive sentiments are more than negative sentiments. By observing the polarity score, the negative sentiments are negligible by seeing the descriptive statistics, less than 3% of customers rated negatively. We performed sentiment analysis of the reviews by using QSR Nvivo 12 software. The positive sentiments are hotel a place of business, location of the hotel, staff, view, service, stay, experience, and the core products offered by hotel [room]. The negative sentiments are place of business, location of the hotel, staff, service, stay, experience, and the core products offered by hotel [room]. We performed text mining analysis using unigram; further analysis can be performed by using N-gram.