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Sentiment Analysis Using Machine Learning Approach

Author

Listed:
  • Andreea-Maria Copaceanu

    (The Bucharest University of Economic Studies)

Abstract

Customers feedback is a valuable asset for businesses, that can be used in order to improve their performance. One of the fastest spreading areas today in computer science - Sentiment Analysis, helps to extract precious information from textual data, in order to identify the feeling of a statement. This research aims to build a classifier to predict customers’ satisfaction, based on Amazon reviews dataset, for different brands of mobile phones. The paper proposes a comparison between four text classification algorithms - Naïve Bayes, Support Vector Machine, Decision Tree and Random Forest, using different feature extraction techniques, such as Bag of words and TF-IDF. In addition, the models are evaluated using accuracy, precision, recall and F-score metrics. Our experiments revealed that Support Vector Machine achieves the best results and is very suitable for classification of the sentiment on product reviews.

Suggested Citation

  • Andreea-Maria Copaceanu, 2021. "Sentiment Analysis Using Machine Learning Approach," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 261-270, August.
    • Handle: RePEc:ovi:oviste:v:xxi:y:2021:i:1:p:261-270
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    References listed on IDEAS

    as
    1. Eman S. Al-Sheikh & Mozaherul Hoque Abul Hasanat, 2018. "Social Media Mining for Assessing Brand Popularity," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 14(1), pages 40-59, January.
    2. Najla M. Alharbi & Norah S. Alghamdi & Eman H. Alkhammash & Jehad F. Al Amri, 2021. "Evaluation of Sentiment Analysis via Word Embedding and RNN Variants for Amazon Online Reviews," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Sentiment analysis; customer reviews; machine learning; text classification;
    All these keywords.

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm

    Statistics

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