sis 23(3): e10

Research Article

Google Maps Data Analysis of Clothing Brands in South Punjab, Pakistan

Download950 downloads
  • @ARTICLE{10.4108/eetsis.v10i3.2677,
        author={Muhammad Ahmad and Kazim Jawad and Muhammad Bux Alvi and Majdah Alvi},
        title={Google Maps Data Analysis of Clothing Brands in South Punjab, Pakistan},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={3},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={1},
        keywords={Sentiment Analysis, Google Maps Data, Clothing Brands, Logistic Regression, Support Vector Machine},
        doi={10.4108/eetsis.v10i3.2677}
    }
    
  • Muhammad Ahmad
    Kazim Jawad
    Muhammad Bux Alvi
    Majdah Alvi
    Year: 2023
    Google Maps Data Analysis of Clothing Brands in South Punjab, Pakistan
    SIS
    EAI
    DOI: 10.4108/eetsis.v10i3.2677
Muhammad Ahmad1, Kazim Jawad1, Muhammad Bux Alvi1,*, Majdah Alvi1
  • 1: Islamia University of Bahawalpur
*Contact email: mbalvi@iub.edu.pk

Abstract

The Internet is a popular and first-hand source of data about products and services. Before buying a product, people try to gain quick insight by scanning through online reviews about a targeted product. However, searching for a product, collecting all the relevant information, and reaching a decision is a tedious task that needs to be automated. Such composed decision-assisting text data analysis systems are not conveniently available worldwide. Such systems are a dream for major cities of South Punjab, such as Bahawalpur, Multan, and Rahimyar khan. This scenario creates a gap that needs to be filled. In this work, the popularity of clothing brands in three cities of south Punjab has been assessed by analysing the brand's popularity using sentiment analysis by prioritizing brands based on organic feedback from their potential customers. This study uses a combination of quantitative and qualitative research to examine online reviews from Google Maps. The task is accomplished by applying machine learning techniques, Logistic Regression (LR), and Support Vector Machine (SVM), on Google Maps reviews data using the n-gram feature extraction approach. The SVM algorithm proved to be better than others with the uni-bi-trigram features extraction method, achieving an average of 80.93% accuracy.