CLASSIFICATION OF REVIEWS, ERROR REPORTS ANDPRODUCT FEATURE REQUESTS USING MACHINE LEARNINGMETHODS

Abstract

This article proposes a solution for filtering and categorizing
user feedback on software products, which can be overwhelming in quantity and
often includes uninformative or fake reviews. The proposed approach involves
using machine learning methods for classifying reviews into categories such as
error reports, product feature requests, and other reviews. The article compares
the performance of different classification ML algorithms and investigates the
impact of preprocessing options on classification accuracy. Additionally, the
article addresses the task of identifying groups of similar reviews in each
category, which can be useful for detecting duplicates and identifying patterns.
The proposed solution is tested on a dataset and compared with existing
solutions. The article concludes by highlighting the novelty and potential
benefits of the proposed approach for improving the quality of user feedback and
enhancing the reputation of software products.