Sentiment Analysis Using Deep Learning Techniques: A Review <s> 1) Applications of Deep Learning: <s> The deep learning paradigm tackles problems on which shallow architectures (e.g. SVM) are affected by the curse of dimensionality. As part of a two-stage learning scheme involving multiple layers of non-linear processing a set of statistically robust features is automatically extracted from the data. The present tutorial introducing the ESANN deep learning special session details the state-of-the-art models and summarizes the current understanding of this learning approach which is a reference for many difficult classification tasks. <s> BIB001 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> 1) Applications of Deep Learning: <s> Abstract In natural language processing community, sentiment classification based on insufficient labeled data is a well-known challenging problem. In this paper, a novel semi-supervised learning algorithm called active deep network (ADN) is proposed to address this problem. First, we propose the semi-supervised learning framework of ADN. ADN is constructed by restricted Boltzmann machines (RBM) with unsupervised learning based on labeled reviews and abundant of unlabeled reviews. Then the constructed structure is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Second, in the semi-supervised learning framework, we apply active learning to identify reviews that should be labeled as training data, then using the selected labeled reviews and all unlabeled reviews to train ADN architecture. Moreover, we combine the information density with ADN, and propose information ADN (IADN) method, which can apply the information density of all unlabeled reviews in choosing the manual labeled reviews. Experiments on five sentiment classification datasets show that ADN and IADN outperform classical semi-supervised learning algorithms, and deep learning techniques applied for sentiment classification. <s> BIB002 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> 1) Applications of Deep Learning: <s> In this paper, we provide an overview of the invited and contributed papers presented at the special session at ICASSP-2013, entitled “New Types of Deep Neural Network Learning for Speech Recognition and Related Applications,” as organized by the authors. We also describe the historical context in which acoustic models based on deep neural networks have been developed. The technical overview of the papers presented in our special session is organized into five ways of improving deep learning methods: (1) better optimization; (2) better types of neural activation function and better network architectures; (3) better ways to determine the myriad hyper-parameters of deep neural networks; (4) more appropriate ways to preprocess speech for deep neural networks; and (5) ways of leveraging multiple languages or dialects that are more easily achieved with deep neural networks than with Gaussian mixture models. <s> BIB003 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> 1) Applications of Deep Learning: <s> There has been a resurgence of research in the design of deep architecture models and learning algorithms, i.e., methods that rely on the extraction of a multilayer representation of the data. Often referred to as deep learning, this topic of research has been building on and contributing to many different research topics, such as neural networks, graphical models, feature learning, unsupervised learning, optimization, pattern recognition, and signal processing. Deep learning is also motivated and inspired by neuroscience and has had a tremendous impact on various applications such as computer vision, speech recognition, and natural language processing. The clearly multidisciplinary nature of deep learning led to a call for papers for a special issue dedicated to learning deep architectures. <s> BIB004
Deep architecture consists of numerous levels of non-linear operations. The capability of modeling the tasks of hard artificial intelligence makes the expectations that deep architecture will be act good in semi supervised learning such as Deep belief network (DBN) and will attain prominent success in Natural language processing community BIB002 . The Deep Learning consists of improved software engineering, enhanced learning procedures and accessibility of computing power and training data BIB003 . It is inspired by neuro science and has splendid impact on a range of applications like speech recognition, NLP (Natural Language Processing) and computer vision. One of the basic challenge of deep learning research is the way of learning the structure of model and the quantity of layers and quantity of hidden variables for each layer BIB004 . While dealing with varying functions, the architecture of deep learning shows full potential and requires labeled samples in high amount for data capturing by deep architecture. Deep learning networks and techniques have been implemented widely in various fields such as in visual classification, pedestrian detection, off-road robot navigation, object categories, acoustic signals and Time series predictions tasks BIB001 . A very motivating approach in natural language processing has explored that complex multi-tasking such as semantic labeling can be highly performed by using deep architectures BIB001 . In terms of data, deep learning efforts to learn high level abstractions by exploiting the hierarchical architectures. It is a promising approach and has been extensively applied in artificial intelligence field, like computer vision, transfer learning, semantic parsing, natural language processing and many more. Now a days, deep learning is prosperous because of three main and important reasons, i.e., improved abilities of chip processing (GPU units), extensively lower expenditure of hardware and significant enhancements in machine learning algorithms  .