The Use of Machine Learning Techniques to Classify Content on the Web

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In text categorization, texts are sorted into groups according to their content. It is the process of automatically classifying texts written in natural languages according to a set of guidelines. Both text comprehension systems, which perform transformations on text such as creating summaries, answering queries, and extracting data, and retrieval of text systems, which collect texts in reaction to a user query on the internet content, rely heavily on text categorization. In order to learn effectively, current algorithms for supervised learning for text classification need a large enough training set. This research introduces a novel text categorization system that makes use of an AI approach and needs fewer articles for training over information found on the web. To generate a feature set from already categorised texts, we resort to "word relation," or association rules based on these terms. The obtained characteristics are then processed by a Support Vector Machine, and ultimately, a single genetic algorithm idea is introduced for classification. The suggested approach has been developed and validated in a working system. The experimental results verify the effectiveness of the proposed system as a text classifier.