Sentiment Analysis Using Deep Learning Techniques: A Review <s> C. Deep Neural Networks (DNN) <s> It is important to classify documents according to their contents because of finding necessary documents efficiently. To achieve good classification document similarity estimation is one of key techniques since classification is executed based on the document similarity. In natural language processing bag-of-words model is used to extract features from documents and term occurrence frequency based value is used as a weight of each features. However, the term weight methodologies usually use predefined models and include some limitations. New approaches to construct feature vectors based on data distribution are desired to achieve high performance of natural language processing. These days many researchers pay attention to deep learning. Deep learning is a new approach to transform raw data to feature vectors using many unlabeled data. This characteristics is desirable to satisfy a previous need. In natural language processing a main aim is to construct a language model on a deep architecture neural network. In this paper we use a deep architecture neural network to estimate document similarity. To obtain good article similarity estimation we have to generate good article vectors that can represent all article characteristics. Hence, we use many stock market news to train the deep architecture neural network and generate article vectors with the trained neural network. And we calculate cosine similarity between labeled articles and discuss performance of the deep architecture neural network. In evaluation we do not focus on articles' contents but on their sentiment polarity. Hence, we discuss whether the proposed method classifies articles according to their sentiment polarity or not. We confirmed though the proposed method is an unsupervised learning approach, it achieves good performance in stock market news similarity estimation. The results show a deep architecture neural network can be applied to more natural language processing tasks. <s> BIB001 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> C. Deep Neural Networks (DNN) <s> In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results. <s> BIB002
In this study BIB002 , author has proposed a model for sentiment analysis considering both visual and textual contents of social networks. This new scheme used deep neural network model such as Denoising auto encoders and skip gram. The base of the scheme was CBOW (Continuous Bag-Of-Words) model. The proposed model consisted of two parts CBOW-LR (logistic regression) for textual contents and was expanded as the CBOW-DA-LR. The classification was done according to the polarity of visual and textual information. Four datasets were evaluated, i.e., Sanders Corpus, Sentiment140, SemEval-2013 and SentiBank Twitter dataset. The proposed model outperformed the CBOWS+SVM and FSLM (fully supervised probabilistic language model). Perhaps the ESLAM (extended fully supervised probabilistic language model) in term of small training data had outperformed the current model. The feature learning and skip grams both required large datasets for best performance. In this study BIB001 , deep neural network architecture has been proposed to evaluate the similarity of documents. The architecture was trained by using several market news to 