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Classifying User Experience (UX) Of The M-Commerce Application Using Multinomial Naive Bayes Algorithm

Published: 05 March 2024 Publication History

Abstract

This research study uses the Multinomial Nave Bayes (MNB) algorithm to categorize and analyze the user experience (UX) of users of mobile commerce applications. The goal of the study is to give business owners insightful information on how well their mobile applications are performing. The study's goals are to establish evaluation standards for categorizing user experiences, use MNB to classify user experience reviews to their appropriate UX elements, analyze the results of the classification, and suggest areas for improvement to enhance the usability of m-commerce. The research plan consists of a number of sprints, including data extraction, data cleaning, classification system creation using the Multinomial Naive Bayes algorithm, and model accuracy rate evaluation. The proposed system integrates the algorithm and uses data from m-commerce applications. The results of the analysis provide insights into the different UX elements such as Value, Adoptability, Desirability, and Usability. The analysis's findings shed light on many UX components like Value, Adoptability, Desirability, and Usability. The classification model was evaluated for accuracy, achieving a result of 89.243%. This means that the model correctly classified 89.243% of the user experience reviews in the evaluation dataset, indicating a satisfactory level of accuracy. However, there were some misclassifications in the remaining 10.757% of the reviews. Therefore, the research successfully developed a system that analyzed and classifies user experiences from customer reviews using MNB. The classification model demonstrated a satisfactory level of accuracy. The findings provide valuable insights and recommendations for improving the mobile application browsing experience based on user feedback and experiences.

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NLPIR '23: Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval
December 2023
336 pages
ISBN:9798400709227
DOI:10.1145/3639233
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 05 March 2024

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  1. Clustering and Classification
  2. User Experience Elements

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