View article

This paper analyzes the performance of text classification using machine learning and deep learning models. The data is preprocessed, followed by text preprocessing. Natural language processing requires the conversion of text data to numerical vectors before they are passed to machine learning or deep learning models. Bag of words and Term Frequency and Inverse Document Frequency (TFIDF) techniques are used for converting text to a numeric vector. The machine learning models demonstrated are Naïve Bayes Classifier and Logistic Regression. Here, the Bag of words is used with Naïve Bayes Classifier, and TFIDF is used with the logistic regression model. The deep learning models demonstrated here are Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The data set used consists of 1786 instances. Train test ratio used is 80:20. The performance of text classification models depends …