SYSTEM OF AUTOMATIC DETERMINATION OF TEXT TONE

Abstract

In the work, the methods of sentiment analysis of the text are considered. The system of emotion detection of Ukrainian-language texts based on dictionaries and rules is proposed. The developed software downloads text information in various formats and carries out tokenization and lemmatization procedures using the Python TokenizeUKand pymorphy2 libraries. As a result, an array of words in the basic grammatical form using for determining the tone of the text is formed. The obtained word base was analyzed using a tonal dictionary of the Ukrainian language. A dictionary of synonyms was used to expand the vocabulary. If there is no word in the tonal dictionary, the tonality value of its nearest synonym is used for further calculations. Computer analysis of textual information was performed in five emotional categories namely: very positive, positive, neutral, negative, and very negative tone of words. To increase the accuracy and validity of sentiment analysis, coefficients were used that take into account the various emotional load of words of different speech parts and their dissimilar impact on the overall assessment of the text tone. The proposed system of sentiment analysis assumes a greater emotional influence of adjectives compared to verbs and nouns. Since the tone of textual information and the expression of human emotions are subjective factors, the means of fuzzy modeling are used for sentiment analysis of texts. This approach makes it possible to take into account the contribution of all emotional categories in the final evaluation of the text. As a result of an aggregation of normalized data on different emotion categories and defuzzification by the method of the center of gravity for one-element sets, a quantitative estimate of the emotional tone of texts was obtained. The developed system of sentiment analysis was tested on Ukrainian-language texts from different sources and different emotional tones. The use of additional tools provides the analysis of more words in the text and the degree of their emotional tone, which leads to more correct detection of the tone of the whole text. Key words : computer text analysis, sentiment analysis, tokenization, tonality dictionary, fuzzy modeling.