Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Amol C. Adamuthe, Department of Information Technology, Rajarambapu Institute of Technology, Rajaramnagar, MS, India, Sneha Jagtap
2019 International Journal of Intelligent Systems and Applications  
This paper presents an investigation of the convolutional neural network (CNN) with Word2Vec word embedding technique for text classification. Performance of CNN is tested on seven benchmark datasets with a different number of classes, training and testing samples. Test classification results obtained from proposed CNN are compared with results of CNN models and other classifiers reported in the literature. Investigation shows that CNN models are better suitable for text classification than
more » ... r techniques. The main objective of the paper is to identify best-fitted parameter values batch size, epochs, activation function, dropout rates and feature maps values. Results of proposed CNN are better than many other classification techniques reported in the literature for Yelp Review Polarity dataset and Amazon Review Polarity dataset. For all the seven datasets, accuracy obtained by proposed CNN is close to the best-known results from the literature. which differs it from traditional machine learning approaches [13] . In literature, CNN effectively solved may application in different domains. General applications of CNN to text classification is a wellstudied topic in the literature. The paper presents convolutional neural network with word embedding technique for text classification. The main objective is divided into three sub-objectives,
doi:10.5815/ijisa.2019.08.06 fatcat:wv7i3y5shzdq7oafcsw3udlhnm