Sentiment analysis is a natural language processing (NLP) technique to identify subjective text in the form of opinion, attitude, and emotion, and classify them as positive, negative, or neutral. Sentiment analysis is an important technique for brand monitoring, market study, and customer feedback analysis, enabling companies to gain insights from text data. Sentiment analysis continues to improve with advancements in deep learning and neural networks, enhancing its precision and applicability. This study offers a comparative study of various sentiment analysis techniques employing the Natural Language Toolkit (NLTK). The evaluation is carried out on the basis of various techniques rather than a single model, reviewing whether or not they are capable of performing well in sentiment classification and harmful content detection. Besides, the study justifies the possibility of creating an online interface by which a user can input a phrase or comment and receive instant feedback on its sentiment and potential harmfulness. This tool can be a valuable in helping brands to analysis effectiveness of their product by analyzing the reviews of customers and comments of their customers on their social media advertisements. Social media sites, customer reviews, and news websites are equally good sources for sentiment analysis, giving organizations a first-hand experience of real-time public opinion feedback. This study examines the importance of sentiment analysis in brand tracking, its techniques, challenges, and ethical issues.