Analyzing tourism reviews using Deep Learning and AI to predict sentiments.


imageToday, it seems natural to discuss Artificial Intelligence or, if you prefer, Machine Learning.Many observers of digital marketing and tourism trends in recent years have not missed an opportunity to emphasize how these technologies will become increasingly prevalent and pervasive in our digital age, especially in the travel and tourism sector.But what are machine learning and deep learning? They are branches of artificial intelligence that provide computers with the ability to ’learn without being explicitly programmed’.In essence, when properly trained, artificial intelligence can respond to novel situations based on reasoning and past experiences.This technology, particularly artificial intelligence in tourism, paves new pathways allowing the automation of tasks that were previously done manually.Even in the hospitality industry, it can be successfully deployed for guest assistance tasks, both before and during their stay.Having an assistant on the site communicating with the visitor in real-time without any delay is an effective way to convert them into a customer.Statistics are confirming a correlation between response time to inquiries and the site’s conversion rate.

Moreover, during the stay, the use of chatbots has proven its worth.Having a continuously active assistant ready to respond is undoubtedly a boon for the establishment.It also includes activities where there’s a need to understand the guest’s requirements and make decisions, such as recommending services to the guest or preparing a quote.Fortunately, the future will not solely consist of chatbots and robots.

A new era of tourism is on the horizon, where humans will spend less time on repetitive tasks and more time catering to and entertaining guests in ways that, for now, only we humans can.In this study, we utilized data from several hotel reviews.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..

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