Analysing tourism reviews using Deep Learning and AI to predict sentiments

The dataset comprised various hotel reviews, with the objective of predicting whether each textual review indicates positive or negative feedback.

The primary challenge was to make predictions solely using the textual data from the reviews.

⇒ a significant correlation between the content of reviews and their overall ratings.

Key: application of AI in automating and enhancing the understanding of customer needs and perceptions in the tourism sector.

Selling point: how AI techniques can be effectively employed to analyse large volumes of textual data, opening new avenues for marketing strategies and service optimisation in the hospitality industry.

The introduction of ‘smart tourism’ and ‘smart destinations’ is deliberate, reflecting an ambition to explore how tourism has evolved into a critical component of national economies.

How do external factors and unforeseen events shape tourism trends.

In what ways can the analysis of hotel reviews, utilizing AI and Deep Learning techniques, enhance our understanding of these patterns and assist in predicting future tourist influxes?

data mining and preprocessing ⇒ transforming raw data into formats that are digestible and actionable

Why important: Tourism has solidified its position as a central pillar within a nation’s economic structure. The appetite for travel and exploration has consistently risen. Yet, this sector has faced intermittent disturbances due to unpredictable influencers and unforeseen events.

AI Application: