THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

Twitter Sentiment Analysis: An Examination of Cybersecurity Attitudes and Behavior

This exploratory study examines the cybersecurity attitudes and actual behavior over time using the data collected on the social media microblogging platform, Twitter. We plan to use the sentiment analysis and text mining techniques on original tweets related to cybersecurity collected at two different time periods. Upon completion of this research, we would present the analysis of the relationship between the cybersecurity attitudes and behavior and how behaviors may be shaped by the attitudes. This research work aims to contribute to the extant literature in cybersecurity and endeavors to enhance our understanding of cybersecurity attitude and behavior by validating the proposed research model and hypotheses by using real-time, user-generated, social media data