A novel self-learning approach to overcome incompatibility on TripAdvisor reviews

Abstract

Among social media networks, TripAdvisor acts as the main role because everyone is eager to share and review their thoughts on their travel experiences in different destinations. Sentiment analysis is a method that can be used to analyze people's behaviors and opinions on public and social media platforms. In this study, hotel reviews are extracted from the five most attractive Sri Lankan cities, and user-written reviews are compared over user bubble ratings, which define overall travelers' experiences as a numerical scale that ranks from 1 to 5. We find that the compatibility between user-written reviews and bubble ratings has a low correlation because bubble ratings may not represent the overall idea of users' genuine opinions expressed in their reviews. To address this problem, a two-phase approach is proposed: (1) the ensemble method to improve the performance of lexicon-based outputs and identify the correctly matching user review and bubble rating; (2) the self-learning approach to finding the sentiment of a review that does not properly label by the user. The performance is studied by considering reviews incompatible with the sentiment of user bubble rating and the sentiment generated by the proposed model. For example, regardless of bigram “not good”, the average percentages of the word “good” for each negatively identified review from the proposed model and bubble rating are 25.63% and 38.85%, respectively. Thereby, it is apparent that the negative sentiments derived by bubble rating have significantly more positive words compared to the proposed model.