| # Details | |
| This model is used for Sentiment Analysis based on BERTurk for Turkish Language https://huggingface.co/dbmdz/bert-base-turkish-cased | |
| # Dataset | |
| 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 (www.beyazperde.com) 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) | |