• Gowthami Vusirikkayala

Automatic allocation of texts into few categories is extremely necessary to analyze the subject of the texts. Hence, text classification acts as an important part in many domains. The issue of allocating a text to a specific type or class is implemented in several methods recently, and with several innovative technological improvements, this class of issue has remarkable resolutions. Apart from the procedure linked to text analysis and parsing methodologies, deep learning has provided a method to resolve this classification condition. This article represents a comparison study on few primary building modules utilized in deep learning, all can be applied to obtain easier frameworks trying to allocate a class of the available texts. The present comparative study presents the effect of these elements can bring the change on the assignment. The computation of the frameworks has been executed on a benchmark existing set of data. The non-linearity offered by these deep learning models are helpful in obtaining high-tech consequences for text classification issue.

Data mining, automation, deep neural network, classification, text

"Text Classification using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.1685-1688, April-2019, Available :http://www.jetir.org/papers/JETIR1904S47.pdf

"Text Classification using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp1685-1688, April-2019, Available at : http://www.jetir.org/papers/JETIR1904S47.pdf