Özet:

Digital information is continuously generated from various sources such as social media, user reviews for services. The processing of this written information to extract user opinions is critical for developing customer satisfaction. In particular, medical services may be improved with customer feedbacks if the user opinions or sentiments are inferred from user reviews. There is an ongoing effort to develop automated software systems to evaluate these customer reviews. Machine Learning (ML) algorithms combined with Natural Language Processing (NLP) techniques are used to assess customer feedbacks. There are many studies related to English language in the literature to evaluate sentiments of user reviews. However, Turkish language needs research and it has abundant search opportunities in terms of sentiment classification. This work develops a soft voting ensemble (SVE) algorithm that combines predictions of Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) to analyze a newly collected medical review data. The accuracies of sentiment classifications of LR, RF and DT are 90.68%, 89.03% and 85.41%. The sentiment classification accuracy of SVE, combination of three algorithms, is 91.12%. The obtained results are promising for an automated Turkish medical sentiment identification algorithm.

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