Monika Zaśko-Zielińska


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Multi-Level Sentiment Analysis of PolEmo 2.0: Extended Corpus of Multi-Domain Consumer Reviews
Jan Kocoń | Piotr Miłkowski | Monika Zaśko-Zielińska
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

In this article we present an extended version of PolEmo – a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).

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Multi-level analysis and recognition of the text sentiment on the example of consumer opinions
Jan Kocoń | Monika Zaśko-Zielińska | Piotr Miłkowski
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

In this article, we present a novel multi-domain dataset of Polish text reviews, annotated with sentiment on different levels: sentences and the whole documents. The annotation was made by linguists in a 2+1 scheme (with inter-annotator agreement analysis). We present a preliminary approach to the classification of labelled data using logistic regression, bidirectional long short-term memory recurrent neural networks (BiLSTM) and bidirectional encoder representations from transformers (BERT).


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A Large Wordnet-based Sentiment Lexicon for Polish
Monika Zaśko-Zielińska | Maciej Piasecki | Stan Szpakowicz
Proceedings of the International Conference Recent Advances in Natural Language Processing