Implementation of robot journalism by programming custombot using tokenization and custom tagging

The paper introduces a prototype of an algorithm that creates personalized news articles about IT and technology based on each personal preference for a specific theme, criteria, or element. When provided a specific personal preference, the algorithm Custombot analyses the data, derives the most appropriate topic that contains the most elements preferred by a person, and eventually produces a one and only news article on that topic. While processing and analysing data by inductive reasoning, Custombot considers the concepts of news angle and filter bubble. Text segmentation (tokenization) and custom tagging are two of the tasks that are used for the construction of this system, allowing to make custom tags and insert matching information in appropriate places. Its result of customized news article can serve as a new service provided by news organizations to satisfy each consumer's needs, and can also be a stepping stone in expanding the role or increasing the importance of robot journalism in a broad field of journalism.