Zhanel Zhexenova


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A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts
Pavlo Seroyizhko | Zhanel Zhexenova | Muhammad Zohaib Shafiq | Fabio Merizzi | Andrea Galassi | Federico Ruggeri
Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)

Cryptocurrencies have gained enormous momentum in finance and are nowadays commonly adopted as a medium of exchange for online payments. After recent events during which GameStop’s stocks were believed to be influenced by WallStreetBets subReddit, Reddit has become a very hot topic on the cryptocurrency market. The influence of public opinions on cryptocurrency price trends has inspired researchers on exploring solutions that integrate such information in crypto price change forecasting. A popular integration technique regards representing social media opinions via sentiment features. However, this research direction is still in its infancy, where a limited number of publicly available datasets with sentiment annotations exists. We propose a novel Bitcoin Reddit Sentiment Dataset, a ready-to-use dataset annotated with state-of-the-art sentiment and emotion recognition. The dataset contains pre-processed Reddit posts and comments about Bitcoin from several domain-related subReddits along with Bitcoin’s financial data. We evaluate several widely adopted neural architectures for crypto price change forecasting. Our results show controversial benefits of sentiment and emotion features advocating for more sophisticated social media integration techniques. We make our dataset publicly available for research.

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Cyrillic-MNIST: a Cyrillic Version of the MNIST Dataset
Bolat Tleubayev | Zhanel Zhexenova | Kenessary Koishybay | Anara Sandygulova
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents a new handwritten dataset, Cyrillic-MNIST, a Cyrillic version of the MNIST dataset, comprising of 121,234 samples of 42 Cyrillic letters. The performance of Cyrillic-MNIST is evaluated using standard deep learning approaches and is compared to the Extended MNIST (EMNIST) dataset. The dataset is available at https://github.com/bolattleubayev/cmnist