saujs Sakarya University Journal of Science 2147-835X Sakarya University 10.16984/saufenbilder.872227 Artificial Intelligence Yapay Zeka Turkish Sentiment Analysis on Social Media https://orcid.org/0000-0002-6262-4244 Kemaloğlu Nazan MEHMET AKİF ERSOY ÜNİVERSİTESİ https://orcid.org/0000-0002-3293-9878 Küçüksille Ecir SULEYMAN DEMIREL UNIVERSITY https://orcid.org/0000-0002-0859-5120 Özgünsür Muhammed Antalya Büyükşehir Belediyesi 06 30 2021 25 3 629 638 02 01 2021 04 01 2021 Copyright © 1997, Sakarya University Journal of Science 1997 Sakarya University Journal of Science

The number of social media users has increased significantly as internet access and internet usage has grown all over the world. As it is the case in any other field, raw data collected from social media platforms are now being transformed into information. Millions of pieces of content are posted every day on platforms such as Twitter, Facebook, and Instagram. Moreover, the ability to extract meaningful information from such content has become an important field of research. This study reports a system for sentiment analysis, based on the data made available on Facebook, Twitter, Instagram, YouTube along with RSS data. Logistic Regression, Random Forest and deep learning algorithm such as Long Short-Term Memory (LSTM) are used to develop the classification model used in the system built as part of this study. Dataset is a new dataset that included data collected from Twitter, used was created and labeled by us. It was found that LSTM model provided the highest accuracy among the models generated using the training dataset. The final version of the model was tested on five different social media platforms and results are communicated.

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