A Comparative Study of Machine Learning Models for Sentiment Analysis: Customer Reviews of E-commerce Platforms

Laleh Davoodi, Jozsef Mezei*

*Corresponding author for this work

    Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

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    Understanding customers' preferences can be vital for companies to improve customer satisfaction. Reviews of products and services written by customers and published on various online platforms offer tremendous potential to gain important insights about customers' opinions. Sentiment classification with various machine learning models has been of great interest to academia and practice for a while, however, the emergence of language transformer models brings forth new avenues of research. In this article, we compare the performance of traditional machine learning models and recently introduced transformer-based techniques on a dataset of customer reviews published on the Trustpilot platform. We found that transformer-based models outperform traditional models, and one can achieve over 98% accuracy. The best performing model shows the same excellent performance independently of the store considered. We also illustrate why it can be sometimes more reliable to use the sentiment polarity assigned by the machine learning model, rather than a numeric rating that is provided by the customer.
    Original languageEnglish
    Title of host publication35th Bled eConference: Digital Restructuring and Human (Re)Action
    EditorsAndreja Pucihar, Mirjana Kljajić Borštnar, Roger Bons, Anand Sheombar, Guido Ongena, Doroteja Vidmar
    Number of pages14
    ISBN (Electronic)978-961-286-616-7
    Publication statusPublished - 23 Jun 2022
    MoE publication typeA4 Article in a conference publication
    EventBled eConference: Digital Restructuring and Human (Re)Action - Bled, Slovenia
    Duration: 26 Jun 202229 Jun 2022
    Conference number: 35th


    ConferenceBled eConference
    Internet address


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