Guven Z.A.2023-01-122023-01-1220219781665429085https://doi.org/10.1109/UBMK52708.2021.9559007https://hdl.handle.net/11454/796976th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods. © 2021 IEEEtr10.1109/UBMK52708.2021.9559007info:eu-repo/semantics/closedAccessE-commerceLanguage ModelMachine LearningProduct ReviewSentiment AnalysisClassifiersComputational linguisticsDecision treesElectronic commerceLogistic regressionMachine learningRandom forestsCommerce platformsE- commercesLanguage modelMachine learning methodsMachine-learningNaive bayesProduct reviewsSentiment analysisTime constraintsTurkishsSentiment analysisConference Object6296322-s2.0-85125864252N/A