<p>Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.</p>

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Sentiment classification is an important but challenging task in natural language processing (NLP) and has been widely used for determining the sentiment polarity from user opinions. And word embedding technique learned from a various contexts to produce same vector representations for words with same contexts and also has been extensively used for NLP tasks. Recurrent neural networks (RNNs) are common deep learning architecture that are extensively used mechanism to address the classification issue of variable-length sentences. In this paper, we analyze to investigate variant-Gated Recurrent Unit (GRU) that includes encoder method to preprocess data and improve the impact of word embedding for sentiment classification. The real contributions of this paper contain the proposal of a novel Two-State GRU, and encoder method to develop an efficient architecture namely (E-TGRU) for sentiment classification. The empirical results demonstrated that GRU model can efficiently acquire the words employment in contexts of user's opinions provided large training data. We evaluated the performance with traditional recurrent models, GRU, LSTM and Bi-LSTM two benchmark datasets, IMDB and Amazon Products Reviews respectively. Results present that: 1) proposed approach (E-TGRU) obtained higher accuracy than three stateof-the-art recurrent approaches; 2) Word2Vec is more effective in handling as word vector in sentiment classification; 3) implementing the network, an imitation strategy shows that our proposed approach is strong for text classification.

As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.