An Efficient Approach for Analyzing Reviews Using an Ensemble Technique

Generally, many Businesses remain failures because of high competition and lack of profits this is because lack of knowledge in that particular area. If we see in Restaurants, e-store and many services have high competition, to make the service unique apart from this competition should know the knowledge and it should be collected from the customers commonly known as Reviews. So, for better understanding we are choosing the restaurant as a source in our project. For this we are designing the system which can collect the reviews from the customers for every item what they ate and then analyse them for producing detailed report for the owner. To resolve the problem of business loss in restaurant point of view we made a solution that is implemented as "An Efficient Approach for Analysing Reviews Using An Ensemble Technique". Each dish at the restaurant is evaluated based on the text review left by the patron, which is then verified as a favorable (positive) or unfavorable (negative) review by the application of a group of classification models (Ensemble ML Model) in machine learning after handling textual data with NLP (to convert textual data to numerical data). And this information is kept in a database where each food item has a number of consumers, a number of favorable reviews, a number of unfavorable reviews, a positive rate and a negative rate. The owner then selects the food item with the lowest rating from the database and does the appropriate quality checks, such as changing chefs or updating the ingredients supplied.