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 Transportation system. 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 and Google Play store. We took a step forward by combining user review texts which were collected from that website to build a model that can predict a review asserting good or bad and Average. Key benefit of our approach is that, by using our proposed model transport system Owners can identify the main focused term from the review of customers and also can take future step to work on that.
We are also able to publish the position of a System by counting that how many reviews are Good , Bad, and Average comparative to with each. As this model is based on text document, it will be very perfect work in all terms and condition. Because text
