Research On Key Problems In Text Classification Research Based On Deep Learning

Text classification is an old issue.This task has since then evolved over the years and new tasks have been created.Over the years,text classification methods have also evolved.Indeed,with the development of technology,the quality and the amount of data used for classification have increased.The large amount of has created the need to change the way data was handled.So,the methods used to handle those data have also changed.Machine learning has appeared to be an efficient tool in achieving text classification.Besides the fact that some of the existing methods are already providing excellent results,the use of text classification keeps on increasing every day.That is the reason why,new methods are investigated to improve the performance of the classification.Deep learning is an emerging subset of machine learning.This paper will be discussing the fundamental research problems in text classification when using deep learning.Different deep learning methods,such as Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM),are explained.In order to achieve our goal,CNN and LSTM are individually chosen to build a text classification program.Naive Bayes(NB)will be our baseline method in order to establish the efficiency of the program built.PyCharm is the development platform.The experiment' results are shown and analyzed.They show that the proposed methods achieved better results than the baseline method.