Machine learning-based sentiment analysis of Twitter data

The paper analyzes the views of Twitter users on the COVID-19 corona virus pandemic based on machine learning algorithms. The role of sentiment analysis increased with the advent of the social network era and the rapid spread of microblogging applications and forums. Social networks are the main sources for gathering information about users’ thoughts on various themes. People spend more time on social media to share their thoughts with others. One of the themes discussed on social networking platforms Twitter is the COVID-19 corona virus pandemic. In the paper, machine learning methods as Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN) are used to analyze the emotional “color” (positive, negative, and neutral) of tweets related to the COVID-19 corona virus pandemic. The experiments are conducted in Python programming using the scikit-learn library. A tweet database related to the COVID-19 corona virus pandemic from the Kaggle website is used for experiments. The RF classifier shows the highest performance in the experiments

Analysis of the current situation and identification of problems in the evaluation of intellectual potential

This paper discusses the theoretical and methodological foundations of the study of the intellectual potential of the population. In the presented research, intellectual potential is the subject of multidisciplinary studies, particularly philosophy, sociology, economics, technology, psychology and pedagogy. To this end, this paper analyzes intellectual potential as a multidisciplinary research field, and examines the main theories, concepts and approaches in this field. Relevant scientific literature on the evaluation of intellectual potential is studied, actual research fields are determined, and their current status is analyzed. The indicators for the evaluation of research activity and efficiency of science in research institutions and organizations are determined. This work explores the international experience in evaluating the activities of scientific organizations, and analyzes the purpose, organization and results of the evaluation in some countries. In the end, it examines the state-of-the-art of scientific potential in the scientific organizations and institutions of Azerbaijan, and presents the important results of the conducted fundamental and applied research and some indicators used in the evaluation of the activity of research organizations

Огляд методів статистичного аналізу багатовимірних даних

We live in the era of "big data". Big data opens up new opportunities for modern society, has become the "raw material" for production, a new source for immense economic and social value. At the same time, big data has set new computational and statistical tasks before the researcher. In order to study the status of these tasks, the paper describes the main applications of big data, investigates the statistical computing problems, associated with a large volume, diversity and high speed, affecting a paradigm shift of statistical and computational methods. A review of existing statistical methods, algorithms and research in recent years is presented. The research results show that several factors require the development of new, more effective statistical methods and algorithms: firstly, traditional statistical methods are not justified in terms of statistical significance with respect to big data; secondly, in terms of computational efficiency; the third factor is relevant to the specific features inherent in big data: heterogeneity, the accumulation of noise, spurious correlations, etc. It appears that this area will continue to be the subject of research.Проблемы статистических вычислений, сопряженные с объемом и многомерностью данных, их разнообразием и высокой скоростью, требуют новых взглядов на парадигмы статистических и вычислительных методов. Проанализированы основные проблемы, связанные с использованием традиционных статистических методов для анализа многомерных данных, и показаны области их применения. Рассмотрены последние методические достижения в области статистики, касающиеся анализа больших данных.Проблеми статистичних обчислень, пов'язані з обсягом і багатомірністю даних, їх різноманітністю і високою швидкістю, та вимагають нових поглядів на парадигми статистичних і обчислювальних методів. Проаналізовано основні проблеми, пов'язані з використанням традиційних статистичних методів для аналізу багатовимірних даних, і показані області їх застосування. Розглянуто останні методичні досягнення в галузі статистики, що стосуються аналізу великих даних

Analysis of intellectual potential measurement indicators

Intellectual potential is the main resource for development and transformation of society. Assessment of intellectual potential is considered as an important factor in increasing the efficiency of the national economy at the modern stage development of the society of information and knowledge economy. This study is dedicated to the analysis of the existing system of indicators used for the assessment of intellectual potential. The main goal of the work is to study the international experience in the field of intellectual potential assessment and the main indicators of intellectual potential measurement. The article examines the main components of intellectual potential (scientific-technical, innovation, educational and cultural potential) and assessment levels (micro, meso and macro). It analyzes the system of indicators offered by several researchers, developed countries and international organizations for the assessment of intellectual potential. It also interprets the intellectual potential assessment indicators of the of higher educational institutions and informs about the international rating systems used for the world universities ranking. Finally, it highlights the indicators system for the assessment of intellectual potential in Azerbaijan and provides recommendations

Review of Statistical Analysis Methods of High-dimensional Data

We live in the era of "big data". Big data opens up new opportunities for modern society, has become the "raw material" for production, a new source for immense economic and social value. At the same time, big data has set new computational and statistical tasks before the researcher. In order to study the status of these tasks, the paper describes the main applications of big data, investigates the statistical computing problems, associated with a large volume, diversity and high speed, affecting a paradigm shift of statistical and computational methods. A review of existing statistical methods, algorithms and research in recent years is presented. The research results show that several factors require the development of new, more effective statistical methods and algorithms: firstly, traditional statistical methods are not justified in terms of statistical significance with respect to big data; secondly, in terms of computational efficiency; the third factor is relevant to the specific features inherent in big data: heterogeneity, the accumulation of noise, spurious correlations, etc. It appears that this area will continue to be the subject of research