Comparative Analysis of Sentiment Analysis Dictionaries: Evaluating the Performance of NLTK, SentiWordNet, TextBlob, and VADER on Hotel Review Sentiment Classification

Sentiment analysis is a critical task for understanding customer opinions and improving service quality in the hospitality industry. This study evaluates the performance of various sentiment analysis dictionaries, including NLTK, SentiWordNet, TextBlob, and VADER, on the task of sentiment classification of hotel reviews. Using a comprehensive dataset of hotel reviews, we preprocess the data and implement these dictionaries for sentiment classification. The performance of each dictionary is assessed using metrics such as accuracy, precision, recall, and F1-score. Our findings provide insights into the effectiveness of these dictionaries and highlight the most suitable approaches for sentiment analysis in the hospitality industry.