Application of Classifiers for Assortment of Online Reviews

Abstract

In Bangladesh, Ecommerce is flourishing day by day especially in the time of crisis the world is facing. There are many platforms available on these sites among which Daraz is the most successful marketplace. This online platform allowed people the ease to do shopping but a large number of reviews and comments made it difficult to opt for the best option. In this paper, the focus is on cataloguing the positive and negative reviews. For this purpose various classifiers were used by using Python. Data cleaning was done and after application of Term Frequency -Inverse Data Frequency with Principal Component analysis it was found that Ridge classifier performed best with more training time then other classifiers and depicted high accuracy. This classifier could help different businesses on different platforms to identify the positive and negative reviews and can provide customers with details about the quality of products.