Please use this identifier to cite or link to this item: https://ruomoplus.lib.uom.gr/handle/8000/1748
Title: A Comparative Study of Sentiment Classification Models for Greek Reviews
Authors: Michailidis, Panagiotis D. 
Author Department Affiliations: Department of Balkan, Slavic & Oriental Studies 
Author School Affiliations: School of Economic and Regional Studies 
Keywords: Greek consumer reviews
large language models
machine learning
neural networks
sentiment analysis
transformers
Issue Date: 1-Sep-2024
Journal: Big data and Cognitive Computing 
ISSN: 2504-2289
Volume: 8
Issue: 9
Start page: 107
Abstract: 
In recent years, people have expressed their opinions and sentiments about products, services, and other issues on social media platforms and review websites. These sentiments are typically classified as either positive or negative based on their text content. Research interest in sentiment analysis for text reviews written in Greek is limited compared to that in English. Existing studies conducted for the Greek language have focused more on posts collected from social media platforms rather than on consumer reviews from e-commerce websites and have primarily used traditional machine learning (ML) methods, with little to no work utilizing advanced methods like neural networks, transfer learning, and large language models. This study addresses this gap by testing the hypothesis that modern methods for sentiment classification, including artificial neural networks (ANNs), transfer learning (TL), and large language models (LLMs), perform better than traditional ML models in analyzing a Greek consumer review dataset. Several classification methods, namely, ML, ANNs, TL, and LLMs, were evaluated and compared using performance metrics on a large collection of Greek product reviews. The empirical findings showed that the GreekBERT and GPT-4 models perform significantly better than traditional ML classifiers, with BERT achieving an accuracy of 96% and GPT-4 reaching 95%, while ANNs showed similar performance to ML models. This study confirms the hypothesis, with the BERT model achieving the highest classification accuracy.
URI: https://ruomoplus.lib.uom.gr/handle/8000/1748
DOI: 10.3390/bdcc8090107
Rights: Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές
Corresponding Item Departments: Department of Balkan, Slavic & Oriental Studies
Appears in Collections:Articles

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