Machine Learning Models for Sentiment Analysis in Brand Management and Consumer Engagement

Sentiment analysis has become an important part of managing brands and getting people to interact with them. It helps businesses figure out what the public thinks, make better experiences for customers, and make smart marketing choices. In this study, different machine learning (ML) models are tested to see how well they work for analyzing brand-related text data from customer polls, product reviews, and social media sites. Many different types of ML methods are compared, including support vector machines (SVM), Random Forest, and Logistic Regression. Also compared are deep learning (DL) structures like long short-term memory networks (LSTM), convolutional neural networks (CNN), and Transformer-based models. To make sure the quality and usefulness of the raw features, the data goes through a lot of strict preparation steps, such as cleaning, normalization, tokenization, stop word removal, and lemmatization. Our method focuses on using the best model training techniques and fine-tuning hyperparameters to get the best performance measures, like F1-score, accuracy, precision, and recall. The test results show that DL models, especially Transformer-based structures, are better than traditional methods at finding complex mood patterns and how they rely on the context of brand-related texts. We also look at the trade-offs between computer complexity and prediction power, giving us ideas that can be used in real-life brand management situations. You can use this thorough review as a help to choose the right mood analysis methods for your business needs. The results help to improve brand image management through data-driven insights, make proactive customer interaction easier, and improve automatic mood recognition systems.