The Twitter Sentiment Analyzer for Hotels is a machine learning-based system developed to analyze customer opinions and sentiments regarding hotel services using Twitter data and hotel reviews. Traditional review platforms may not always capture genuine customer experiences, whereas social media provides more raw and unfiltered feedback. The project utilizes Natural Language Processing (NLP) techniques to process and classify customer sentiments as positive or negative based on tweets and review data. By training the model on over 501,000 hotel reviews, the system achieved an accuracy of 87% while maintaining efficient processing time. The platform helps hotels understand customer satisfaction levels, identify service weaknesses, and improve overall customer experience through data-driven insights.