An AI Bot Who Is Suggesting Words to Create Trending Social Media Posts

Abstract

Today, social media is the mainstay of many advertising campaigns. As an example, facebook, youtube social media are widely used by TV and radio channels to advertise their programs. Not only that but many higher education institutions and even business organizations use social media extensively to reach out to a wider audience. At the same time, more and more of these advertising agencies are emerging than ever before. Despite spending so much money on social media, it can be seen that only selected advertisements reach the masses. The main reason for this is that although many people advertise on social media, they do not have a good understanding of how to do it correctly using the correct keywords. As a solution to this, before placing such an advertisement or any post on social media, if there is a prior understanding of how the product or service should be advertised these days and what words and pictures should be used for the post, then advertising is most effectively can be done. Therefore, the purpose of this project, which author is going to carry out, is to create a website for those who want to study the trending information in the social media, using artificial intelligence technology and want to do a new publicity. In other words author has created a system to get suggested keywords for social media posts according to the relevant category what should be included in their new posts to be a trending post. The author has collected data & information to archive this task related to trending social media posts for various categories and then has done a prediction by considering the amount of reaches, likes and also the comments. In this project author has used two models with linear regression and multi linear regression based AI techniques. In methodology chapter author has clearly mentioned about them. After used newly created system it was identified that new system has an increment of user reactions than using the traditional posting methods. Therefore following the testing of the new system, user responses to posts created using keywords derived from the new system were found to be higher than the responses to posts created in the normal way, and more details are contained in testing & evaluation chapter.