Using machine learning to predict the users ratings on tripAdvisor based on their reviews

Abstract

Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we intend to facilitate the acquiring information process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning algorithms (Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbors and Recurrent Neural Networks) and Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. The experiments carried out showed that the developed models can predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users.