Convolutional neural networks for text categorization with latent semantic analysis
Sojwal Patil, Aishwarya Gune, Mayura Nene · 2017
The recent emphasis on intelligent systems has increased the focus on categorization techniques as it is an important step in information retrieval and natural language processing. The text categorization is largely achieved using machine learning techniques. In most of the approaches one-hot encoding or pre-trained word embedding such as word2vec or glove vectors are used. This study explores the feature vectors based encoding using Latent Semantic Analysis (LSA) technique along with the Convolutional Neural Network (CNN) being used as a classifier. It was found that applying LSA followed by CNN for text classification offers better accuracy than the conventional methods of CNN with other approaches. This research, thus, highlights the importance of Latent Semantic Analysis technique coupled with convolutional neural networks for text classification.