Mining Twitter Data for Business Intelligence Using Naive Bayes Algorithm for Sentiment Analysis

Today social media has grown to be a big player in the way businesses and organizations operate, especially with the coronavirus pandemic increasing the online footprint of organizations. The use of data from social media to drive business intelligence is now of growing interest to both researchers and business owners. Business owners can now utilize platforms like Twitter to learn about their target audience and improve their business processes to meet their growing needs. Twitter makes it easy to see what is going or about to go viral and vital details like why it is going viral and the players behind it. This research aims to help business owners’ especially small and medium enterprises and start-ups gain a competitive advantage in their industry by using the "crowd wisdom" opportunity via social media. The proposed system is based on Twitter and crawls the platform for relevant data, including; locations, trends, and important actors (influencers) within a specified field; the system cleans the data and presents the information in an actionable format. Python was used for Twitter data mining, and sentiment analysis of the tweets was done using Naive Bayes classifiers