Abstract:
In recent years it is noticeable that sharing text reviews on various businesses specially restaurants
through website and social media is a very common phenomenon. Online reviews reflect user’s
opinion. This huge collection of user data in terms of text reviews can be analyzed to identify user’s
sentiment and their demand also. Here users are the primary sources. Text reviews are the complete
reflection of user’s sentiment and also owned by them.
Measuring user’s sentiment will also be able to find out the market position of a Restaurant. By
making the machine learned about the total reviews, it will be able to categorize the unknown text.
we collect the necessary data for our research work from a verified source. we took a step forward
by combining user review texts which were collected from that website to build a model that can
give some idea about the topics about what users think the most while writing a review on any
restaurant.
Key benefit of our approach is that, by using our proposed Topic model, Owners can identify the
main focused term from the review of customers and also can take future step to work on that. As
this model is based on text document, it will be very perfect work in all terms and condition.
