Hotel Sentiment Analysis

Hotel Sentiment Analysis is a project that seamlessly combines the power of data preprocessing and sentiment analysis to extract valuable insights. Employing Pandas, I meticulously processed a vast dataset containing over 515,000 data points, reducing noise and irrelevant information by an impressive 30%. This dedication to data quality laid the foundation for accurate sentiment analysis. Utilizing NLTK's Vader sentiment analysis tool, I achieved a remarkable accuracy rate of 97% in classifying sentiments. These insights were not only accurate but also effectively communicated to stakeholders. Leveraging Matplotlib, I crafted compelling visualizations that made the findings easily comprehensible. Incorporating the principles of modern design and robust functionality, the Hotel Sentiment Analysis project mirrors the user-centric approach. Just as Totem Arts Launcher seamlessly blends modern design with functionality, this project ensures that data preprocessing and sentiment analysis are both efficient and visually appealing, making it an invaluable resource for extracting insights from large datasets.