Aspect Based Opinion Mining on Hotel Reviews

Abstract

Users often use online reviews to assess the quality of hotels according to their various attributes. In this study, a sentiment analysis of online reviews has been conducted using eleven attributes the most frequently reviewed pertaining to hotels. Using this analysis, users' overall assessments of hotels have been determined and summarized from reviews left for a group of various hotels. To identify words with similar meanings to the eleven predetermined hotel attributes, the Word2Vec method has been employed. Additionally, the FastText method has been used to detect words containing spelling errors. The sentiment analysis of the comments has been made by using three different methods belonging to two different approaches. These methods are VADER method as dictionary-based approach, BERT and RoBERTa as machine learning approaches. Using these methods, the reviews have been evaluated in three categories as positive, negative, and neutral, and the quality score has been calculated. In addition, a software with a user-friendly graphical interface has been implemented in an effort to easily use all the methods used in this study.