Best Restaurant Review and Opinion Mining Rating - T. Jayasri, V. Vaneesha, U. Mohan Srinivas, K. Venkataramana, Shravya Chidurala (Mathematical Statistician and Engineering Applications, 2023)
Here, we suggest a cutting-edge approach for reviewing restaurants that recognizes covert client feelings and assigns ratings in line with those findings. For the system to perform as intended, opinion mining methodology is used. A online tool called Opinion Mining for Restaurant Reviews analyses the uploaded reviews. The system collects user feedback and determines whether a restaurant is good, poor, or worst based on those opinions. We score keywords from user comments based on their sentiment using a database of sentiment-based keywords and a weighting system for positivity or negativity. Once logged in, the user can view restaurants and provide reviews after visiting them. The system will use a database, compare user feedback to database keywords, and rank the user feedback. The administrator’s job is to add keywords to the database and post new restaurants. Finally, a customized selection of services is being offered to customers through the use of recommendation programmers. Or, to put it another way, they are designed to generate suggestions (for eateries or tourist destinations, for example) that are suited to the demands of the customer and may be applied to a range of circumstances. A number of useful data management techniques may be utilized to improve the effectiveness and efficiency of recommendation processes as well as to address any potential issues. This article presents a machine learning strategy. To the problem of tailoring restaurant tastes based on search results from TripAdvisor.com. The hotel’s amenities are utilized, and visitor feedback is taken into account. Each hotel has natural language processing (NLP) integrated to analyze and classify all previous user reviews (good or bad) for each hotel. The overall percentage of comments is then calculated and recorded. Before receiving recommendations, users must first choose the aspects. The right hotels are then found, and the user feedback is analyzed to determine which hotel receives the highest ratings.The guest is ultimately directed to the top-rated hotel by the framework the restaurant suggests. The proposed sentimental score metric, which is based on the NLP algorithm, is used to analyze the emotions and traits of user comments. Natural Language Processing (NLP) is a machine learning technology that makes use of human language to analyze, interpret, and infer meaning in a clever and efficient manner.The recommended NLP method outperforms the ones currently in use, it was found. The results of the evaluation back this up. The study paper’s reader will get a more thorough and precise list of neighboring restaurants. The study’s conclusions and suggestions show that the procedure is highly accurate.