Disneyland Reviews Sentiment Analysis

This project performs sentiment analysis on a dataset containing 42,000 reviews from three Disneyland branches: Paris, California, and Hong Kong. The reviews are sourced from Trip Advisor and include various features such as ratings, review text, and reviewer location. Download the "DisneylandReviews.csv" dataset from the google drive folder to be able to run.

Data Set

• Drive: https://drive.google.com/drive/folders/1XacGiTkJjVTRfmnzsf2w8P8pgfVaNHPi?usp=sharing

Features

Data Exploration:

Analyze the distribution of reviews across different Disneyland branches and visualize trends using Seaborn and Matplotlib. Sentiment

Classification:

Utilize machine learning techniques, specifically the Naive Bayes algorithm, to classify reviews as positive, negative, or neutral based on the text content.

TF-IDF Vectorization:

Implement TF-IDF (Term Frequency-Inverse Document Frequency) to transform the review text into a format suitable for machine learning. Dataset

The dataset includes the following columns:

• Review_ID: Unique identifier for each review
• Rating: Rating from 1 (unsatisfied) to 5 (satisfied)
• Year_Month: Date of the review
• Reviewer_Location: Country of the reviewer
• Review_Text: The text content of the review
• Disneyland_Branch: The branch of Disneyland being reviewed