Тональність художньої прози як вияв особливостей перекладу

Abstract

The paper is devoted to the peculiarities of sentiment analysis of texts. The necessity to take into account the tone as an important component of the text’s content to achieve equivalence in literary translation and ensure the preservation of the emotional and expressive background and expressiveness of the original is substantiated. The importance of automating the processes of detecting the text’s tone to obtain better and more accurate results is determined. The paper outlines the main methods of preprocessing textual information and identifies a set of the most popular machine learning approaches that can be used for tone classification. In particular, the attention is focused on the Bag-of-Words, TF-IDF Vectorizer, and Word2Vec models used to convert textual data into numerical data. Classifier algorithms are analysed as a further step in data processing (in particular, Bayesian, maximum entropy, support vectors, etc.). The advantages of the LIWC-22 software, which has proven itself and has been successfully tested with the involvement of experts in psychology, sociology and linguistics, are described separately. Using the above-mentioned lexicon, a sentiment analysis of three versions of Y. Vynnychuk's short story («The Embroidered World») translations into English (author's translation by M. Naidan and automated translations by Google and DeepL) is carried out. The total number of words is established, and the indicators of authenticity and tone (in the binary coordinate system «positive/negative») of each translation option are determined. Prospects for further research in the field of sentiment analysis for the interpretation of original and translated texts are formulated.