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This model is used for Sentiment Analysis based on BERTurk for Turkish Language


We used product and movie dataset provided by a study [2] . This dataset includes movie and product reviews. The products are book, DVD, electronics, and kitchen. The movie dataset is taken from a cinema Web page ( with 5331 positive and 5331 negative sentences. Reviews in the Web page are marked in scale from 0 to 5 by the users who made the reviews. The study considered a review sentiment positive if the rating is equal to or bigger than 4, and negative if it is less or equal to 2. They also built Turkish product review dataset from an online retailer Web page. They constructed benchmark dataset consisting of reviews regarding some products (book, DVD, etc.). Likewise, reviews are marked in the range from 1 to 5, and majority class of reviews are 5. Each category has 700 positive and 700 negative reviews in which average rating of negative reviews is 2.27 and of positive reviews is 4.5.

The dataset is used by following papers

1 Yildirim, Savaş. (2020). Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language. 10.1007/978-981-15-1216-2_12. 2 Demirtas, Erkin and Mykola Pechenizkiy. 2013. Cross-lingual polarity detection with machine translation. In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM ’13)