Recommendation Systems on Twitter Data for Marketing Purposes using Content-Based Filtering

With artificial intelligence and machine learning enhancement over time, many concepts in commerce and marketing have changed. Today, many people are using online shopping options since online platforms provide a wide variety of products. Vendors also try to attract customers and offer them user-specific products on online shopping platforms using many technologies. One of these technologies is Recommendation Systems (RS) which makes information filtering on batch data. RS makes shopping very easy for both customers and vendors. While it suggests products they may like or prefer for customers, it also provides a target customer list for vendors. RS can also be applied to many social media platforms for marketing purposes. In this paper, we propose an RS analyzing Twitter data for vendors. In our RS, vendors search some keywords to extract target Twitter users as customers, and then it lists the corresponding Twitter users using the Content-Based Filtering technique. Our systems succeeded to recommend the target customer list with 86. 24 % accuracy.