Over the past decade, sentiment analysis has
emerged as a pivotal tool in tourism-related texts, driven
by the sheer volume of tourist attractions and the wealth
of online information. Tourists increasingly turn to travel
websites to access specific information that often eludes
standard evaluations of tourist attractions. Forums
particularly illuminate specific information needs
and their ties to potential destinations. Among these
platforms, TripAdvisor has become a favoured choice
for posting reviews, ratings, and facilitating online
bookings. In this context, this study aims to analyse
and assess sentiment in reviews sourced from the online
platform TripAdvisor, focusing on tourist attractions in
the northern Portuguese destination of Bragança. The
research spotlights the disparity between qualitative and
quantitative rankings. The study also underscores the
importance of data pre-processing, including removing
irrelevant information and stop words. Pre-processing
was crucial in refining sentiment prediction accuracy,
highlighting the differentiated roles of these words
in context and meaning. Despite utilising advanced
techniques such as tokenisation, TF-IDF weighting,
logistic regression, and n-grams, the study‘s models
encountered challenges in achieving high accuracy in
sentiment prediction. Even the incorporation of bigrams
did not yield substantial performance improvements,
with the models frequently inclined to overestimate
negative and positive sentiments.
