• Vishnu Panickar
• Sujit Pradhan
• Priyanka Kashyap
• Ashish Kawale
• Dr. Nihar M. Ranjan

Documents are one of the most common methods for maintaining data and records. Everyday a lot of documents/files are generated with lot of data for future research purposes or for business analytics. These files/documents must should be stored effectively so that it can be retrieved whenever needed. Organizing large documents can be a tedious task as the internal content of the files are not known. Manually organizing each and every file is not practically possible as it may take hours to categorize a file based on its contents and also the accuracy of classification cannot be guaranteed. In the fields like Library Science a huge amount of files are required to be maintained, which can be helpful in future for business decisions or for research purpose. To make this task easier Text Document Classifier can be used. Text Document Classifier can classify a given document based on the contents inside the document and label the document from the pre-defined classes. Unlike traditional classification Techniques in Machine Learning like Support Vector Machine, term frequency-identification and Naïve Bayes Classifier, Neural Networks has better analytical results. Traditional Classification Methods has limitations in terms of effective feature extraction and the dimensionality problem, these limitations can be solved by Convolutional Neural Networks.

Convolutional Neural Network, Data Mining, Machine Learning, Text Classification, Word Embeddings.

"Text Document Classification using Convolutional Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.329-333, June-2020, Available :http://www.jetir.org/papers/JETIR2006387.pdf

"Text Document Classification using Convolutional Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp329-333, June-2020, Available at : http://www.jetir.org/papers/JETIR2006387.pdf