Sentiment Analysis for Brand Management Using Machine Learning Techniques

In this digital age, brands are being evaluated and defined more by what people say about them on social media networks, particular twitter. As a sub-field of Natural Language Processing (NLP), sentiment analysis provides an effective way of brand assessing consumer attitudes. In this paper, the author will discuss the application of machine learning (Logistic Regression, Random Forest, and Support Vector Machine (SVM)) to Twitter data in identifying and interpreting sentiments about a brand. The experiment includes the fine preprocessing of tweet texts, extracting features based on TF-IDF, training and evaluating models. Focusing on the comparative analysis, the results show that SVM classifier performs better than others with an accuracy of 71.09 which is slightly higher than the Logistic Regression (70.31) and Random Forest (69.58). The results are backed with classification reports, confusion matrices, and plots. The study aims at highlighting the practical use of machine learning in brand management, which can help monitor brand feedback in real-time and make strategic decisions based on consumer reactions.

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