Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/56726
Title: Sentiment Analysis of Turkish Drug Reviews with Bidirectional Encoder Representations from Transformers
Authors: Bozuyla, M.
Keywords: bidirectional transformer
drug review
Turkish
word embedding
Deep learning
Diagnosis
Embeddings
Health care
Quality control
Signal encoding
Adverse drug reactions
Bidirectional transformer
Drug review
Embeddings
Healthcare services
Personalized therapies
Sentiment analysis
Turkishs
User-generated
Word embedding
Sentiment analysis
Publisher: Association for Computing Machinery
Abstract: Sentiment analysis of user generated product or service reviews is significant to enhance quality. Healthcare related computational linguistics studies, particularly analysis of drug based user criticisms, have principal importance above all. Sentiment analysis of healthcare reviews reveal the relations between patients, doc- tors and healthcare services. More specifically, sentiment analysis of drug reviews may be used to acquire relations such as adverse drug reactions (ADRs), diagnosis-treatment assist, and personalized therapy recom- mendations. Most of the drug review sentiment studies are in English. Though Turkish is a widely spoken language, there is limited research conducted on medical domain and there is particularly no study related to drug review sentiment analysis. In this study, we generated a Turkish drug review dataset and we eval- uated the generated dataset in detail against (i) traditional machine learning algorithms with language pre- processing steps, stemming and feature selection, (ii) deep learning algorithms with word2vec embedding language model, and (iii) various bidirectional encoder representations from transformers (BERT) models in terms of sentiment analysis. The experiments show that neural transformers are promising in Turkish drug review sentiment identification. In particular, Turkish dedicated BERT (BERTurk) resulted in 95.1% weighted- F1 score as the best drug review sentiment prediction performance. © 2024 Association for Computing Machinery. All rights reserved.
URI: https://doi.org/10.1145/3626523
https://hdl.handle.net/11499/56726
ISSN: 2375-4699
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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