Twitter Data: Text Categorization and Grouping for Business Analytics

Abstract

Twitter has become a potent forum for consumers to interact with brands, share information, and voice their opinions in the age of social media domination. Businesses hoping to use the massive volume of textual data produced by Twitter to inform their decision-making have a number of opportunities as well as problems. By arranging, evaluating, and classifying this material in a meaningful manner, text classification and clustering approaches can offer insightful information.Text classification helps organisations comprehend attitudes, thoughts, or themes related to their brand or products by applying predefined categories or labels to tweets. Businesses can assess consumer contentment, pinpoint areas for development, or assess the results of marketing initiatives by utilising sentiment analysis algorithms to extract the sentiment conveyed in tweets. However, without the need for pre-established categories, text clustering makes it possible to find patterns or groups within the Twitter data. Businesses can gather insights into developing trends, customer groups, or communities of interest by using it to find naturally occurring clusters of tweets based on their content. These clusters can be used to target particular client groups, provide product recommendations, and personalise marketing campaigns.