In the era of social media dominance, Twitter has emerged as a powerful platform for users to express their opinions, share information, and engage with brands. The vast amount of textual data generated on Twitter presents both opportunities and challenges for businesses looking to leverage this information for effective decision-making. Text classification and clustering techniques can provide valuable insights by organizing, analyzing, and categorizing this data in a meaningful way. Text classification involves assigning predefined categories or labels to tweets, enabling businesses to understand sentiments, opinions, or topics associated with their brand or products. By applying sentiment analysis algorithms, businesses can determine the sentiment expressed in tweets, helping them gauge customer satisfaction, identify areas of improvement, or evaluate the impact of marketing campaigns. Text clustering, on the other hand, enables the identification of patterns or groups within the Twitter data without pre-defined categories. It allows businesses to discover natural groupings of tweets based on their content, allowing them to gain insights into emerging trends, customer segments, or communities of interest. These clusters can be used to personalize marketing strategies, recommend products, or target specific customer groups.

Twitter, Sentiment Analysis, Decision Tree, k-means, Social Media