Sneha Jagtap

Work place: Department of Computer Engineering, Rajarambapu Institute of Technology, Rajaramnagar, MS, India



Research Interests: Computer systems and computational processes, Computational Learning Theory, Computer Architecture and Organization, Data Structures and Algorithms


Sneha N. Jagtap completed M. Tech. in Computer Science and Engineering from Rajarambapu Institute of Technology, Rajaramnagar in year the 2018. Her areas of interest are text classification, machine learning and web development.

Author Articles
Comparative Study of Convolutional Neural Network with Word Embedding Technique for Text Classification

By Amol C. Adamuthe Sneha Jagtap

DOI:, Pub. Date: 8 Aug. 2019

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 other 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.

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