Textual Classification Utilizing the Integration of Semantics and Statistical Methodology

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Effectively classifying texts is possible using several classification techniques. Machine learning constructs a classifier by studying and memorising the characteristics of several classes. For text categorization, deep learning provides similar advantages since it can function with great precision using simpler architecture and processing. In order to categorise textual information, this research makes use of machine learning and deep learning methods. There is a great deal of extraneous details in textual data that must be removed during pre-processing. To prepare it for analysis, we remove duplicate columns and impute missing data. In the next step, we use deep learning techniques for classification, including long short-term memory (LSTM), artificial neural network (ANN), as well as gated recurrent unit (GRU). According to the findings, GRU obtains 92% accuracy, which is higher than that of any other model or baseline investigation.