The purpose of this paper is to develop a hybrid model Ukrainian language sentiment analyzer, which should improve the accuracy of the mood definition to expand the Ukrainian language among the instruments on the market. The object of research is the processes of determining the language of the text and predicting its sentiment score. The subject of the study is Ukrainian comments posted by Google Maps users. The following text categories are taken into account: food, hotels, museums, and shops. The new method was built as an ensemble of support vector machine, logistic regression, and XGBoost, in combination with a rule-based algorithm. The practical use of the algorithm makes it possible to analyze the Ukrainian text in accordance with the category with the visualization of the research results. The accuracy of the proposed method is bigger than 0.88 in the worst case. The mining procedure of the positive and negative sides of service providers based on users’ feedback is developed. It allows electronics business to make improvements based on frequent positive and negative words.

The Sentiment Analysis Model of Services Providers’ Feedback

The issue of authorship attribution has long been considered and continues to be a popular topic. Because of advances in digital computers, this field has experienced rapid developments in the last decade. In this article, a survey of recent advances in authorship attribution in text mining is presented. This survey focuses on authorship attribution methods that are statistically or computationally supported as opposed to traditional literary approaches. The main aspects covered include the changes in research topics over time, basic feature metrics, machine learning techniques, and the advantages and disadvantages of each approach. Moreover, the corpus size, number of candidates, data imbalance, and result description, all of which pose challenges in authorship attribution, are discussed to inform future work.

A review on authorship attribution in text mining

This article dwells process of ML-model development for detecting paraphrasing by binary classification of texts pair. For this study, the following semantic similarity metrics or indicators have been selected as features: Jacquard coefficient for shared N-grams, cosine distance between vector representations of sentences, Word Mover Distance, distances according to WordNet dictionaries, prediction of two ML-models: Siamese neural network based on recurrent and Transformer type - RoBERTa. Developed software uses principle of model stacking and feature engineering. Additional features indicate semantic affiliation of sentences or normalized number of common N-grams. Created model shows excellent classification results on PAWS test data: weighted accuracy (precision) – 93%, weighted completeness (recall) – 92%, F-measure (F1-score) – 92%, accuracy (accuracy) – 92%. Results of study have shown that Transformer-type NNs can be successfully applied to detect paraphrasing in a pair of texts with fairly high accuracy without need for additional feature generation. Fine-tuned NN RoBERTa (with additional fully connected layers) is less sensitive to pairs of sentences that are not paraphrases of each other. This model specificity may contribute to incorrect accusations of plagiarism or incorrect association of user-generated content. Additional features increase both overall classification accuracy and model sensitivity to pairs of sentences that are not paraphrases of each other.

Machine Learning Model for Paraphrases Detection Based on Text Content Pair Binary Classification

The novelty of the research is an offered combination of the machine learning method – the data clustering and the classical method – the Student’s t-test to differentiate English and Ukrainian texts. The efficiency of the two methods has been proved to be high for determining the style factor effect and the authorial style factor effect. The research allows us to conclude that the data clustering is a simpler method than the Student’s t-test, but it ensures essential differences in fewer cases than the Student’s t-test. The use of the Student’s t-test is more complicated as it can be performed only after the Pearson’s normality test. However, with the help of the Student’s t-test, the essential differences have been established in most cases with a test validity of 95%. The research shows that the proposed combination of methods ensures reliable results. The obtained results may be used for text analysis and authorship attribution.

Machine Learning and Classical Methods Combined for Text Differentiation

Businesses should come up with a strategy, plans, and goals so that their total assets can make a profit during the transformation process. Utilizing various features of a property can generate this income. This comparison provides evidence of profitability. During the global economic downturn, a number of businesses encountered issues that caused their payment situations and profitability to deteriorate. The goal of this article is to ascertain whether particular profitability indicators also revealed the pandemic-related global crisis, particularly in the Visegrad Group countries. This analysis was conducted based on categories of business size. Specifically, 8671 enterprises were analyzed. The evaluation of indicators revealed whether there was a significant change in a negative direction, a significant change in a positive direction, or no significant change. It was possible to make a clear diagram of the companies that took part in the study and to figure out the median values in order to compare the results of the chosen profitability indicators. Correspondence analysis was conducted so that conclusions could be more accurate. According to the findings of this study, indicators of ROA, ROE, and ROS did not change significantly across enterprise size categories in the years preceding, during, and after the pandemic. Since the government regulations of the V4 countries had a significant impact on these businesses, the change was most obvious in the case of small businesses within the ROS indicator. The added value of the article is derived from its analysis of selected profitability indicators in the largest group of Central European nations and its relevance.

/pdf/does-the-size-of-the-business-still-matter-or-is-u246e8dv.pdf

Does the Size of the Business Still Matter, or Is Profitability under New Management, by Order of the COVID-19?

A one-consonant group approach to the authorship attribution has been proposed. The approach is based on determining, by the chi-square test, the consonant group in which the difference between the texts by different authors is statistically significant. The developed model determines author-differentiating capability of each consonant group in a relation of the number of comparisons, in which the difference between the texts by two authors is statistically significant to the total number of comparisons. The determined general author-differentiating capability of the group of stop consonants, which is a statistical parameter of the authorial style, is the highest in the comparisons of texts from the publicist and belles-lettres styles. The one-consonant group approach simplifies the whole process of authorship attribution and ensures a higher level of automation. The conducted experiments on the Java programming language have proved that the chi-square test is a powerful nonparametric statistical test that can be used for author identification on the level of English consonants with a test validity of 95%.

/pdf/software-based-approach-towards-automated-authorship-44m161ipg5.pdf

Software-Based Approach towards Automated Authorship Acknowledgement—Chi-Square Test on One Consonant Group

The developed mathematical support for authorship attribution software includes a combination of statistical methods (Student’s t-test, Kolmogorov-Smirnov’s test) and a statistical model for determining significant differences between styles. The combination of statistical methods allows us to enhance test validity of authorship attribution by obtaining the same results by the two methods applied. The model developed makes it possible to identify a consonant phoneme group with high style identification capability. The phoneme position in a word is taken into account. The greater number of significant differences is, the higher authorship identification capability of the phoneme group is. The developed system software is based on the algorithms of the used combination of methods and statistical model. The Java programming language provides platform independence. The minimized number of consonant phoneme groups makes the process of style and authorship attribution more automated. The obtained results of comparisons of the scientific, belles-lettres, conversational and newspaper styles are presented. The data obtained allows us to assert that the used combination of methods and the developed statistical model improve test validity of style and authorship attribution.

/pdf/approach-for-minimization-of-phoneme-groups-in-authorship-3p7elyz1y8.pdf

Approach for minimization of phoneme groups in authorship attribution

The novelty of the research is the method of a combination of an analysis of the absolute and relative frequencies of occurrence of letters of Ukrainian and the chi-square test for identification of Ivan Franko’s authorial style in the literary works ”Na loni pryrody’ and “Malyy Myron”. According to the results, typical of Ivan Franko’s authorial style are the letters of Ukrainian, the values of absolute frequencies of occurrence of which differ in less than 10 units (б, з, т, у, ф, х, ч, ц, ш, щ, ю). The same percentage has been obtained for the relative frequencies of occurrence of the following letters: б, в, г, д, ж, з, к, у, ф, ч, ц, ш, ь, ю, я. The applied statistical method – the chi-square test has shown that the differences between the literary works ”Na loni pryrody’ and “Malyy Myron” are not statistically significant and consequently, the works are written by the same author. The results have proved that the chi-square test is efficient for solving the authorship attribution task on the level of letters. The test validity is 95%. The used software is Python and R. The practical application of the research is identification of the authorial style on the level of letters.

The Statistical Parameters of Ivan Franko’s Authorial Style Determined by the Chi-square Test

The problem of improvement of the software for authorial style identification is topical today and requires new approaches. The proposed approach consists in the use of the efficient classical and machine learning methods which ensure reliable data with a test validity of 95%. These are the following methods: the chi-square test and the discriminant analysis. The methods have been applied on the level of letters of Cyrillic alphabet which proved to be appropriate for an author identification task. Typical statistical characteristics have been established for Ukrainian authors’ styles. With the help of these characteristics, the author can be identified. The proposed structure of the software system is the novelty of the research. The developed software system is based on a modular principle. The algorithm of the author’s identification is realized on the Python programming language. The level of automation is high.

Automated Identification of Authorial Styles

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