A Model Configuration of Social Media Text Mining for Projecting the Online-Commerce Transaction (Case: Twitter Tweets Scraping)

Text mining is a process to extract data based on text. The source of text mining should be coming from social media users' activities; e.g. posting, fans page, group, hashtag, tweet, etc. As raw data and information, the text mining sources are able to be gold information to be operated for academic analyzing purposes. This paper proposes the Python-based model to scrape text from Twitter user's tweets and then analyze them to see the commerce and tax-income potency in purposely Indonesia. The mathematical and statistical methods are technically operated. The constructed model finally exhibited. Also, the information on sale-behavior and tax-income forecasts are able to be portrayed.