Abstract
Most commercial websites, such as Amazon, encourage users to leave reviews of the goods and services they get after purchasing them. For certain consumers, this analysis is critical when determining whether or not to buy a product. Understanding the consequences of feedback and correctly classifying their utility may therefore be an advantageous method for websites. The classification results can also be used as a review and recommendation program for ongoing success. Nowadays people visit several restaurants on various occasions. They get confused most of the time after having a look at the food menu. Based on the ratings and reviews of the dish it becomes easier for them to decide the dish they wish to order. However they unable to read each review of the previous customers. So to overcome this issue, we have proposed NLP (Natural Language Processing) technique and Spacy CNN (Convolutional Neural Network) pipeline system which will classify all the reviews in a single rating. Each review is labelled with a reviewer's score indicating the sentiment of the reviewers. Our task is to predict a reviewer’s score on a scale of 0 or 1. Where 1 indicates the users like the dish while 0 indicates that the reviewers were not satisfied with the dish.
Highlights
People visit several restaurants on various occasions. They get confused most of the time after having a look at the food menu
The challenge at hand is to perform a binary classification using a mixture of text-based functionality and machine learning algorithms
In Conclusion, the rating system don’t work independently to provide how the product is useful to the consumer but with the help of review classification, we can generate rating with the help of users review
Summary
People visit several restaurants on various occasions. They get confused most of the time after having a look at the food menu. Based on the ratings and reviews of the dish it becomes easier for them to decide the dish they wish to order. It is impossible for them to read each and every review of the previous customers [6],[9]. This proposal aims to create an automatic text-based classification model that can forecast the usefulness of Zomato, Swiggy and Reddit feedback accurately. The challenge at hand is to perform a binary classification using a mixture of text-based functionality and machine learning algorithms. Tokenization, translating uppercase to lowercase, lemmatization, and other text-based features were used to create this model [8]
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