This study explores the use of machine learning (ML) and deep learning (DL) models for predicting stock price movements through sentiment analysis of financial news articles. Four models were evaluated: Random Forest (RF), Gradient Boosting (GB), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). The results showed that deep learning models, particularly BERT, outperformed traditional ML models, achieving higher accuracy, precision, recall, and F1 scores. BERT’s ability to capture contextual relationships in text proved superior in handling the complexities of financial news. This research highlights the effectiveness of sentiment analysis in stock market prediction and suggests that advanced ML and DL techniques can enhance forecasting accuracy. Future work could focus on refining these models by integrating more data sources and exploring hybrid approaches.

This article discusses the problem of the role of the cluster in the process of forming a sustainable business and maintaining the innovative development of the national economy, including its individual industries in modern conditions. In the course of writing the article, various analytical methods were used: comparative and retrospective analysis, the method of data compilation, induction and deduction, as well as modeling. As a result of a generalized analysis of the activities of clusters and, in general, the activities of groups of companies, it was concluded that the importance of cluster associations is significant in the overall structure of mechanisms to support the development of innovations. The author analyzed various approaches to the formation of cluster policy by determining the effect of the resulting synergetic features of the cluster form of doing business, from the point of view of the formation of business sustainability and strengthening the position of the company's brand.

In this study, we conducted a comparative analysis of traditional machine learning models and advanced deep learning models for sentiment analysis of consumer feedback, aiming to assess their impact on business strategies. We evaluated the performance of Random Forest, Support Vector Machines (SVM), Naive Bayes, BERT, and GPT models using a comprehensive dataset derived from e-commerce platforms, social media comments, customer surveys, and online forums. Our results demonstrated that while traditional models like Random Forest and SVM achieved decent accuracy, they were outperformed by the large language models, BERT and GPT. BERT achieved the highest accuracy (92.7%), precision (91.3%), recall (94.2%), and F1-score (92.7%), showcasing its exceptional ability to capture contextual relationships in text. GPT also demonstrated strong performance with an accuracy of 91.5%, although slightly lower than BERT. The findings suggest that transformer-based models, particularly BERT, offer significant advantages in processing consumer feedback, enabling businesses to extract more accurate insights for decision-making, customer satisfaction improvement, and marketing optimization. This study emphasizes the importance of adopting deep learning models for sentiment analysis in business contexts while acknowledging the potential limitations related to computational resources. Ultimately, our research highlights the value of sentiment analysis in informing business strategies and enhancing customer engagement.

In this study, we conducted a comparative analysis of traditional machine learning models and advanced deep learning models for sentiment analysis of consumer feedback, aiming to assess their impact on business strategies. We evaluated the performance of Random Forest, Support Vector Machines (SVM), Naive Bayes, BERT, and GPT models using a comprehensive dataset derived from e-commerce platforms, social media comments, customer surveys, and online forums. Our results demonstrated that while traditional models like Random Forest and SVM achieved decent accuracy, they were outperformed by the large language models, BERT and GPT. BERT achieved the highest accuracy (92.7%), precision (91.3%), recall (94.2%), and F1-score (92.7%), showcasing its exceptional ability to capture contextual relationships in text. GPT also demonstrated strong performance with an accuracy of 91.5%, although slightly lower than BERT. The findings suggest that transformer-based models, particularly BERT, offer significant advantages in processing consumer feedback, enabling businesses to extract more accurate insights for decision-making, customer satisfaction improvement, and marketing optimization. This study emphasizes the importance of adopting deep learning models for sentiment analysis in business contexts while acknowledging the potential limitations related to computational resources. Ultimately, our research highlights the value of sentiment analysis in informing business strategies and enhancing customer engagement.

The article discusses the need to develop a marketing strategy for the automobile industry of the Republic of Uzbekistan, proposes a model of marketing strategy, as well as a standard model for determining the regression weight coefficient. Key words: strategy, marketing strategy, marketing strategy development models, marketing strategy structure, regression weight coefficients.

AI-enabled business analytics has become a game changer in the US tech industry by making it possible for organizations to gain innovation, greater efficiency and competitive advantage, especially in product development. The main focus of this study is on the adoption of more advanced AI techniques like machine learning (ML), natural language processing (NLP), predictive analytics and computer vision to take a look at the impact they have on the four core product development metrics (development speed, product quality, innovation potential and customer satisfaction). The project used a quantitative research design through which data were gathered from 200 U.S.-based tech professionals through a structured survey to better understand organizational practices, outcomes and challenges. Results indicate substantial changes in outcomes of product development, with machine learning emerging as the technique with most impact, especially covering customer satisfaction and predictive capability. Smaller firms observed that not having enough resources, not having enough skilled personnel and even not being capable of achieving data confidentiality are making it difficult for them to adopt AI and, as a result, they rated their satisfaction less than larger organizations, which have already been in front of their technological revolution and possess advanced infrastructure and readiness for AI. Organizational readiness, size and strategic alignment were found to be the significant predictors of AI success and statistical analyses such as chi-square tests, regression analysis and correlation, verified these findings. The study underscores the critical need for pairing AI initiatives with business goals, so that the results are optimized. The fact remains that the potential for transformation exists and there is still a barrier for the smaller companies in this area who struggle with integration and scalability. Filling a critical gap in the empirical research on AI driven business analytics, this study provides useful insights for policymakers and practitioners involved in the U.S. tech industry in practice while providing a future research agenda for emerging AI technologies that contribute to product development.

The present study seeks to investigate the influence of social media on purchasing decision-making, as understanding the role of social media in the context of marketing is extremely important for researchers, companies, and customers alike. Social media platforms play an increasingly significant role in shaping customers' purchasing decisions, as they provide customers with access to information about products and services, the ability to share their experiences with others, and direct communication with brands. The data were analyzed using statistical software SPSS v.26 and AMOS v.26. The findings indicated a robust positive correlation between social media and purchasing decision-making. The practical results revealed a significant impact of social media on the marketing purchase decision.

The role and importance of metropolitan transport in the socio-economic development of the country are great, not only ensuring economic stability, but also significantly improving the lifestyle of the population. The metropolitan system is important for a wide range of goals, including increasing transport efficiency, improving the environmental situation, as well as developing urban infrastructure and ensuring social equality. Role in socio-economic development - the metropolitan system contributes to the economic development of the country in several ways.

This scientific article describes the role and importance of institutional investors in developing the capital market of the Republic of Uzbekistan. The dynamics of the development of the corporate bond market are analyzed. Practical data on corporate bonds placed by institutional investors, their interest rates, nominal value and maturity dates are analyzed. Conclusions are presented on ensuring macroeconomic stability through developing the bond market in Uzbekistan.