Analyzing tourism reviews using Deep Learning and AI to predict sentiments

In this study, we investigate the application of Artificial Intelligence (AI), specifically through Deep Learning and neural networks, in analyzing and predicting sentiments expressed in tourism reviews. Our dataset comprised various hotel reviews, with the objective to predict whether each textual review indicates positive or negative feedback. The primary challenge was to use solely the textual data of the reviews for this prediction. Through meticulous data processing and analysis, we developed neural network-based models that highlight the efficacy of Deep Learning in the accurate interpretation of reviews. Our findings reveal a significant correlation between the content of reviews and their overall ratings, thereby providing new insights into the application of AI in automating and enhancing understanding of customer needs and perceptions in the tourism sector. The principal contribution of this study is the practical demonstration of how AI techniques can be effectively employed to analyze large volumes of textual data, opening new avenues for marketing strategies and service optimization in the hospitality industry. Each review represents a client’s assessment of a hotel. For each textual review, we aim to predict whether it corresponds to a positive review (the customer is satisfied) or a negative review (the customer is dissatisfied). The overall ratings of the reviews can range from 2.5/10 to 10/10. To simplify the issue, we’ll categorize them as follows: negative reviews have overall ratings of less than 5; positive reviews have ratings of 5 or higher. The challenge lies in predicting this information using only the raw textual data of the review.