Sentence Classification using Machine Learning with Term Frequency–Inverse Document Frequency with N-Gram

Abstract

Automatic text classification has proven to be a vital method for managing and processing a very large text area—the volume of digital materials that is spreading and growing on a daily basis. In general, text plays an important role in classifying, extracting, and summarizing information, searching for text, and answering questions. This paper demonstrates machine learning techniques are used for the text classification process..And also, with the vast rapid growth of text analysis in all areas, the demand for automatic text classification has widely improved by day by day. The pattern of text classification has been the subject of a lot of research and development works in recent times of natural language processing is a field that entails a lot of work. This paper represents a text classification technique using the term frequency-inverse document frequency and N-Gram. Also compared the performances of a different model. The recommended model is adopted with four different algorithms and compared with generated results from the algorithms. The linear support vector machine is most relevant to this work with our proposed model. The final result shows a significant accuracy compared with earlier methods.