Tourist Review Sentiment Analysis

01 · Purpose

Every review becomes part of a destination’s story

Tourist places are shaped not only by maps and marketing, but by the experiences people describe after visiting. This project explored whether machine learning could classify those reviews and help summarize public sentiment around destinations.

The practical idea was to turn a large collection of opinions into a signal that could support tourism planning and understanding.

02 · Building the dataset

Collecting language from different review pages

The project created a dataset from multiple tourist review pages. The text was prepared for modeling and converted into numerical features using Count Vectorizer and TF-IDF.

This was my first deeper look at how raw language becomes model input, and how preprocessing choices influence what the model is able to learn.

03 · Model comparison

Testing different ways to read the same text

I implemented and compared Naive Bayes, Support Vector Machine, and Random Forest classifiers. Performance was reviewed through accuracy, precision, recall, and F1-score rather than a single metric.

The final system reported 86% prediction accuracy and included a simple interface for sentiment classification.

04 · Reflection

Language carries more than positive or negative labels

The project worked at a basic sentiment level, but real travel experiences are more nuanced. A review can praise the location while criticizing access, price, safety, or service.

That limitation made me interested in aspect-based sentiment and the difference between classifying an opinion and actually understanding it.

05 · The next question

From sentiment to destination intelligence

A future version could identify the specific aspects behind each opinion and track how sentiment changes by season or event. Images, geospatial data, and temporal patterns could add a richer view of visitor experience.

The goal would be to help destination managers understand not only whether people were satisfied, but why.