Kamil Kanclerz, Konrad Karanowski, Julita Bielaniewicz, Marcin Gruza, Piotr Miłkowski, Jan Kocon, Przemyslaw Kazienko. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Many tasks in natural language processing like offensive, toxic, or emotional text classification are subjective by nature. Humans tend to perceive textual content in their own individual way. Existing methods commonly rely on the agreed output values, the same for all consumers. Here, we propose personalized solutions to subjective tasks. Our four new deep learning models take into account not only the content but also the specificity of a given human. The models represent different approaches to learning the representation and processing data about text readers. The experiments were carried out on four datasets: Wikipedia discussion texts labelled with attack, aggression, and toxicity, as well as opinions annotated with ten numerical emotional categories. Emotional data was considered as multivariate regression (multitask), whereas Wikipedia data as independent classifications. All our models based on human biases and their representations significantly improve the prediction quality in subjective tasks evaluated from the individual’s perspective.

In this article, we present a novel technique for the use of language-agnostic sentence representations to adapt the model trained on texts in Polish (as a low-resource language) to recognize polarity in texts in other (high-resource) languages. The first model focuses on the creation of a language-agnostic representation of each sentence. The second one aims to predict the sentiment of the text based on these sentence representations. Besides models evaluation on PolEmo 1.0 Sentiment Corpus, we also conduct a proof of concept for using a deep neural network model trained only on language-agnostic embeddings of texts in Polish to predict the sentiment of the texts in MultiEmo-Test 1.0 Sentiment Corpus, containing PolEmo 1.0 test datasets translated into eight different languages: Dutch, English, French, German, Italian, Portuguese, Russian and Spanish. Both corpora are publicly available under a Creative Commons copyright license.

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 Bidirectional Long Short-Term Memory (BiL-STM) and Bidirectional Encoder Representations from Transformers (BERT).