Abstract This article aims to (1) identify guests’ memorable experiences based on reviews posted TripAdvisor, (2) the differences in due hotel location, evaluation TripAdvisor and consumer sentiment. The study used quantitative methods: text mining, topic modelling, sentiment analysis. All (n = 34,992) for all Warsaw hotels included (N 99) were analysed. Seven topics of identified via Latent Di...

Online social networking site are the most popular sites on the internet. The second generation of web based services is characterized by having a consumer generated content (CGC), which allow people to share information. This paper examines CGC on TripAdvisor, with a case study on the city of Lisbon. Along with a discussion on the radical changes implied by new forms of collaboration and busin...

Online review systems (ORS) such as TripAdvisor or Yelp collect numeric evaluations from reviewers using interval scales. However, the UI of interval scales differ remarkably across ORS, even though prior research suggests that design cues of the interval scale can bias individual’s interpretation of the scale and thus the numeric evaluations. The impact of the UI on numeric evaluations is part...

 Many Internet sites and media companies (Amazon.com, YouTube, Netflix, Yahoo, Tripadvisor, Last.fm, IMDb) are developing and deploying RSs as part of the services they provide to their subscribers;  At institutions of higher education around the world, undergraduate and graduate courses are dedicated entirely to RSs; tutorials on RSs are very popular at computer science conferences;  There ...

Recommender systems have become an important tool for users to identify interesting items as well as for businesses to promote their products to the right users. With the rapid development of social networks, travelers start to seek recommendations and advises from websites like TripAdvisor and Yelp. While travelers are willing to share their opinions on social networks, this provides an opport...

In this project we explored varieties of supervised machine learning methods for the purpose of sentiment analysis on TripAdvisor hotel reviews. We experimented and explored with the factors that affect accuracy of the predictions to develop a satisfying review analyzer. We focus on not only the overall opinions but also aspect based opinions including service, rooms, location, value, cleanline...

Travellers prefer to rely on peers’ recommendations and review websites look for reliable unbiased information. Trustworthy communicators are more influential than the untrustworthy one. content is need of hour in specific post COVID19 travel. This research analysing what propels a traveller towards destinations empirically verify valences trustworthiness TripAdvisor reviews. Content analysis r...