Monika Zaśko-Zielińska


2019

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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).

2018

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Towards Emotive Annotation in plWordNet 4.0
Monika Zaśko-Zielińska | Maciej Piasecki
Proceedings of the 9th Global Wordnet Conference

The paper presents an approach to building a very large emotive lexicon for Polish based on plWordNet. An expanded annotation model is discussed, in which lexical units (word senses) are annotated with basic emotions, fundamental human values and sentiment polarisation. The annotation process is performed manually in the 2+1 scheme by pairs of linguists and psychologies. Guidelines referring to the usage in corpora, substitution tests as well linguistic properties of lexical units (e.g. derivational associations) are discussed. Application of the model in a substantial extension of the emotive annotation of plWordNet is presented. The achieved high inter-annotator agreement shows that with relatively small workload a promising emotive resource can be created.

2015

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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