Yahya Younas

Recommendation System Using Sentimental Analysis In Tourism The project title is "Recommendation System Using Sentiment Analysis In Tourism", a fully software-oriented web-based final year project. HTML5, CSS3, JavaScript (React JS) in the front-end, Python (Flask) and libraries (Scikit-learn, Pandas, and (NLTK) in the backend, and MongoDB as a database, are used in this project. The target audience of the system are those travelers who may travel seldom or frequently. The proposed system deals with the hotel reviews and feedback of their customers about the services provided by the hotels. To generate the right recommendations for the travelers, an intelligent approach i.e., collaborative filtering is used in the project. The system deals with large-sized data to fulfill the needs of travelers. The proposed system recommends the hotels based on the hotel features and guest type for a personalized recommendation. Real-world datasets of different hotel websites were used to train the sentiment analysis model. The flow of the project starts with user input criteria i.e., location, No. of travelers, etc. from UI. Then our system gets data from external sources and filters this data by user’s criteria. Store this data into a database and get a column of reviews from the database. After getting reviews, call the sentiment analysis module with reviews as parameters. Now in the Sentiment Model (SM), preprocess the data (reviews) including tokenization, part of speech tagging, stop word removal, etc. Then extract features from the review and assign them a weight. After feature extraction and weight assignment, the next step is classification. Then return the scores of hotels to the recommender module. The recommender module performs sorting based on scores and displays recommendations. Some of the screenshots of the project are attached below: Admin Panel User Panel