Вісники НТУ "ХПІ"

Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/2494


З 1961 р. у ХПІ видається збірник наукових праць "Вісник Харківського політехнічного інституту".
Згідно до наказу ректора № 158-1 від 07.05.2001 року "Про упорядкування видання вісника НТУ "ХПІ", збірник був перейменований у Вісник Національного Технічного Університету "ХПІ".
Вісник Національного технічного університету "Харківський політехнічний інститут" включено до переліку спеціалізованих видань ВАК України і виходить по серіях, що відображають наукові напрямки діяльності вчених університету та потенційних здобувачів вчених ступенів та звань.
Зараз налічується 30 діючих тематичних редколегій. Вісник друкує статті як співробітників НТУ "ХПІ", так і статті авторів інших наукових закладів України та зарубіжжя, які представлені у даному розділі.

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2019 - 201912020 - 20231

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Ключові слова: search.filters.subject.text classification

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Зараз показуємо 1 - 2 з 2
  • Ескіз
    Документ
    Using long short-term memory networks for natural language processing
    (Національний технічний університет "Харківський політехнічний інститут", 2023) Onyshchenko, Kostiantyn; Daniiel, Yana
    The problem of emotion classification is a complex and non-trivial task of language interpretation due to the natural language structure and its dynamic nature. The significance of the study is in covering the important issue of automatic processing of client feedbacks, collecting opinions and trendcatching. In this work, a number of existing solutions for emotion classification problem were considered, having their shortcomings and advantages illustrated. The evaluation of performance of the considered models was conducted on emotion classification on four emotion classes, namely Happy, Sad, Angry and Others. The model for emotion classification in three-sentence conversations was proposed in this work. The model is based on smileys and word embeddings with domain specificity in state of art conversations on the Internet. The importance of taking into account the information extracted from smileys as an additional data source of emotional coloring is investigated. The model performance is evaluated and compared with language processing model BERT (Bidirectional Encoder Representations from Transformers). The proposed model achieved better performance at classifying emotions comparing to BERT (having F1 score as 78 versus 75). It should be noted, that further study should be performed to enhance the processing by the model of mixed reviews represented by emotion class Others. However, modern performance of models for language representation and understanding did not achieve the human performance. There is a variety of factors to consider when choosing the word embeddings and training methods to design the model architecture.
  • Ескіз
    Документ
    Efficiency estimation of methods for sentiment analysis of social network messages
    (Національний технічний університет "Харківський політехнічний інститут", 2019) Borysova, N. V.; Melnyk, K. V.
    The results of effectiveness evaluating of machine learning methods for sentiment analysis of social network messages are presented in this paper. The importance of the sentiment analysis problem as one of the important tasks of natural language processing in general and text ual information processing in particular is substantiated. A review of existing methods and software for sentiment analysis are ma de. The choice of classifiers for sentiment analysis of texts for this research is substantiated. The principles of functioning of a Naïve Bayesian Classifier and classifier based on a recurrent neural network are described. Classifiers were sequentially trained in two corpuses: first, in the RuTweetCorp corpus, the corpus of short messages from the social network Twitter, and then on the Slang corpus, the corpus of messages from social networks Facebook and Instagram and posts from the Pikabu website, second corpus have been marked up the tonality of slang words. Information about the tonality of slang words was taken from the youth slang dictionary obtained as a result of the survey of users. The separation of texts by tonality was carried out into three c lasses: positive, negative and neutral. The efficiency of these classifiers was evaluated. Efficiency evaluation was carried out according to standard metrics Recall, Precision, F-measure, Accuracy. For the naive Bayesian classifier, after training on the first corpus, the following metric values were obtained: Recall = 0,853; Precision = 0,869; F-measure = 0,861; Accuracy = 0,855; and after training on the second corpus such values were obtained: Recall = 0,948; Precision = 0,975; F-measure = 0,961; Accuracy = 0,960. For the classifier based on a recurrent neural network, after training on the first corpus, the following metric values were obtained: Recall = 0,870; Precision = 0,878; F-measure = 0,874; Accuracy = 0,861; and after training on the second corpus such values were obtained: Recall = 0,965; Precision = 0,982; F-measure = 0,973; Accuracy = 0,973. These results prove that additional training on the second corpus increased the efficiency of classifiers by 10–11%.