Recommendation System using Sentimental Analysis in Tourism
In recent times, travelers have to face a lot of problems in the selection of a suitable hotel according to their needs for reserving a room. The reason behind such problems is having not enough knowledge about the well-known hotels in the vicinity at destinations. Internet-based search has made it
2025-06-28 16:28:56 - Adil Khan
Recommendation System using Sentimental Analysis in Tourism
Project Area of Specialization Artificial IntelligenceProject SummaryIn recent times, travelers have to face a lot of problems in the selection of a suitable hotel according to their needs for reserving a room. The reason behind such problems is having not enough knowledge about the well-known hotels in the vicinity at destinations. Internet-based search has made it easy to choose any hotel worldwide. However, travelers have to go through huge and unnecessary information in locating and reserving space in close vicinity hotels. Therefore, recommender systems can play an important role in their significant decisions to select and reserve space quite easily. In this work, we proposed a recommender system, named RSSAT (Recommendation System using Sentimental Analysis in Tourism).RSSAT used the reviews of hotels given by their customers. In order to generate accurate recommendations, an intelligent approach has been considered to accommodate large-sized data to fulfill the needs of travelers. In RSSAT, the Collaborative Filtering approach has been used to generate recommendations. RSSAT recommends the hotels based on the hotel features and guest type for a personalized recommendation. A dataset of different hotels containing the reviews has been used to train the model. The results obtained by the model have been considered in terms of accuracy. RSSAT used the results to finalize the list of recommendations.
Project ObjectivesThe goal is to know the reputation of hotels and recommend the best suitable hotel to the user that fulfills their needs as well as have a good reputation among the other travelers by using sentiment analysis on reviews. By doing so, the problems present in the older recommendation systems that only follow the rating system will be countered. Rating is not enough to give the right information about the reputation of a hotel as reviews give detailed information about the hotel. RSSAT (Recommendation System using Sentimental Analysis in Tourism) will provide suggestions and recommendations which are purely based on the traveler’s preference and choice.
The objective is to fulfill the need of travelers and the problems present in the older recommendation systems. In RSSAT, we will keenly focus on the quality of the system, and this will be achieved by ensuring that the suggestions and recommendations are efficient and according to the traveler's needs. By using RSSAT, the tourist selects a perfect hotel that not only fulfills their requirements or needs but also has a good reputation. RSSAT will recommend all the well-known hotels according to traveler preferences. Travelers can find a suitable hotel according to their needs as we perform sentiment analysis on reviews to generate recommendations.
Project Implementation MethodScrum methodology is used because RSSAT (Recommendation System using Sentimental Analysis in Tourism) will be developed in the form of increments to incorporate changes if suggested by the supervisor. We can easily get breakpoints so we can change on the advice of the supervisor.
In RSSAT, the recommender module used the SM to recommend the list of hotels that will be displayed on the UI. We required users reviews as well as complete hotel information for RSSAT. MongoDB is used to store the data of hotels. Big size dataset is used for training the SM so that it can give an accurate output. We select a dataset from KAGGLE (website for datasets) by Jiashen Liu [13] for the training of the model. The data present in the dataset is noisy, so we have to preprocess the data to make it useful for SM.
- RSSAT (Recommendation System using Sentimental Analysis in Tourism) system will help in achieving true hotel recommendations through processing and analyzing a large dataset of reviews using data mining and producing relevant recommendations according to traveler’s type and choice.
- Development of a web-based recommender system for hotel recommendation which integrates linked data of different hotels containing their information available online.
- The proposed approach optimizes the performance in the proposed hotel recommendation system using Mongo DB in React environment.
- The dataset of reviews will be obtained from external sources (Hotel Websites) to help in the project.
With an increase in the number of comments and reviews of users on websites or social networks, extracting users’ preferences by sentiment analysis has become realizable. We did not require to store the user’s previous search history or book history for a recommendation. So, RSSAT (Recommendation System using Sentimental Analysis in Tourism) consists of two modules including the recommender system and the sentiment analysis module.
User input criteria i.e., location, number of travelers, etc. Then RSSAT will get data from external sources and filter data that matches the 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. In SM, preprocess the data (reviews) including tokenization, part of speech tagging, stop word removing, etc. Then extract features from the review and assign them a weight. After feature extraction and weight assignment, the next step is classification. And then compute the score on the basis of weight and no. of time it appears. Then our SM gives reviews results with a calculated score to the recommender module where it sorts the hotel's list according to their score and displays all the hotel lists as a recommendation.
Final Deliverable of the Project Software SystemCore Industry ITOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Industry, Innovation and InfrastructureRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 41000 | |||
| Documentation Printing | Miscellaneous | 2 | 500 | 1000 |
| Hotel's API | Equipment | 1 | 40000 | 40000 |