Enhancing user experience through advanced sentiment analysis

Abstract

The rapid growth of user-generated textual content on social media platforms, messaging applications, e-commerce websites, and online services has increased the importance of automated sentiment analysis for understanding public opinion and improving digital services. The aim of this research is to investigate modern Natural Language Processing (NLP) approaches for sentiment analysis and to develop an effective method for analyzing and summarizing consumer reviews in order to enhance user experience. The scientific significance of the study lies in exploring advanced machine learning and deep learning techniques for processing large volumes of unstructured textual data and identifying emotional patterns in user feedback. The research methodology includes text preprocessing, tokenization, vectorization using transformer-based representations, and sentiment classification using Long Short-Term Memory (LSTM) models and machine learning algorithms. The results show that deep learning models improve the accuracy of sentiment detection and enable more reliable analysis of user opinions expressed in textual data. The findings contribute to the development of efficient analytical tools for sentiment analysis and provide practical support for businesses in understanding consumer preferences and improving the quality of digital services.