Emerging Technologies in Computing. Second International Conference, iCETiC 2019, London, UK, August 19–20, 2019, Proceedings

Research Article

Sentiment Analysis in E-commerce Using SVM on Roman Urdu Text

  • @INPROCEEDINGS{10.1007/978-3-030-23943-5_16,
        author={Faiza Noor and Maheen Bakhtyar and Junaid Baber},
        title={Sentiment Analysis in E-commerce Using SVM on Roman Urdu Text},
        proceedings={Emerging Technologies in Computing. Second International Conference, iCETiC 2019, London, UK, August 19--20, 2019, Proceedings},
        proceedings_a={ICETIC},
        year={2019},
        month={7},
        keywords={Roman Urdu Sentiment analysis Opinion mining SVM E-commerce Reviews},
        doi={10.1007/978-3-030-23943-5_16}
    }
    
  • Faiza Noor
    Maheen Bakhtyar
    Junaid Baber
    Year: 2019
    Sentiment Analysis in E-commerce Using SVM on Roman Urdu Text
    ICETIC
    Springer
    DOI: 10.1007/978-3-030-23943-5_16
Faiza Noor1,*, Maheen Bakhtyar1,*, Junaid Baber1,*
  • 1: University of Balochistan
*Contact email: noorfaiza84@gmail.com, maheenbakhtyar@um.uob.edu.pk, junaidbaber@ieee.org

Abstract

The usefulness and importance of sentiment analysis task is a widely discussed and effective technique in e-commerce. E-commerce is a very convenient way to buy things online. It saves a lot of time that is usually spent traveling and buying by visiting the shops. E-commerce provides an efficient and effective way to shop sitting right in front of one’s computer/mobile at home. For a given product, sentiment analysis captures the users views; their feelings and opinion related to that product. The reviews are categorized into three basic classes i.e. negative, positive, and neutral. This paper focuses on that are obtained by one of the most famous and accessed e-commerce website of . There are total 20.286 K reviews which are annotated into three classes by three different experts. Vector space model, a.k.a bag of word model is applied for feature extraction which are later passed to Support Vector Machines (SVM) for sentiment classification. Experiments are conducted on MATLAB Linux server. The dataset is kept public for future use and experiments.