Sentiment Analysis Using Deep Learning Techniques: A Review <s> E. Deep Belief Networks (DBN) <s> We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities. <s> BIB001 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> E. Deep Belief Networks (DBN) <s> Due to the complexity of human languages, most of sentiment classification algorithms are suffered from a huge-scale dimension of vocabularies which are mostly noisy and redundant. Deep Belief Networks (DBN) tackle this problem by learning useful information in input corpus with their several hidden layers. Unfortunately, DBN is a time-consuming and computationally expensive process for large-scale applications. In this paper, a semi-supervised learning algorithm, called Deep Belief Networks with Feature Selection (DBNFS) is developed. Using our chi-squared based feature selection, the complexity of the vocabulary input is decreased since some irrelevant features are filtered which makes the learning phase of DBN more efficient. The experimental results of our proposed DBNFS shows that the proposed DBNFS can achieve higher classification accuracy and can speed up training time compared with others well-known semi-supervised learning algorithms. <s> BIB002 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> E. Deep Belief Networks (DBN) <s> NOTE FROM ACM: It has been determined that this article plagiarized the contents of a previously published paper. Therefore ACM has shut off access to this paper. This article has been removed from the ACM Digital Library because it was found to plagiarize an earlier work written by Xiangbo Shu, Guo-Jin Qi, Jinhui Tang, and Jingdong Wang published by ACM and entitled DOI:http://doi.acm.org/10.1145/2733373.2806216 Weakly-Shared Deep Transder Networks for Heterogeneous-Domain Knowledge Propagation. In Proceedings of the 23rd ACM International Conference on Multimedia (MM '15). ACM, New York, NY, USA, 35-44. For further information, contact the ACM Director of Publications. <s> BIB003
Deep belief networks (DBNs) BIB002 includes several hidden layers, composed by RBM (restricted Boltzmann machines). DBN has been proved efficient for feature representation. It utilizes the unlabeled data and fulfills the deficiencies of labeled analysis issues. In this paper BIB003 , a new deep neural network structure has been presented termed as WSDNNs (Weakly Shared Deep Neural Networks). The purpose of WSDNNs is to facilitate two languages to share sentiment labels. The features of language specific and inter language have been presented through building multiple weakly shared layers of features. The datasets from Prettenhofer and Stein have been used containing four languages French, German, English and Japanese. In comparison with existing studies the proposed work address the challenge of shortening overlap among feature spaces of both source and target language data through cross lingual information transfer process using backpropogation. DNNs used for transformation of information from source to target language. The experiments have been conducted for sentiment classification tasks of cross multilingual product reviews of Amazon. results concluded that the proposed approach is more effective and powerful in terms of cross lingual sentiment classification than the previous studies. Another study by BIB001 has used deep belief network with word vector for the political detection in Korean articles. The proposed model has used SVM for bias calculation, five stage pipeline for detection of political bias, python web crawler to gather news articles, KKMA for morpheme analysis, word2Vec and scikit-learn package. The dataset contained 50,000 political articles from 01 Jan, 2014 to 28 Feb, 2015 . Results showed 81.8% accuracy by correctly predicting labels and the results contained mean square error of 0.120. This research BIB002 has proposed a deep belief network with feature selection (DBNFS) to overcome the vocabulary problems, the network has used input corpus along with numerous hidden layers. Chi-Squared technique of feature selection was used to improve the Deep Belief Network (DBN) for the purpose of decreasing complexity of vocabulary input and for eliminating irrelevant features. By applying ChiSquared technique, the learning phase of DBN was enhanced to DBNFS. In this work, two new tasks features, selection and reduction were used along with many other tasks of existing classification approaches, such as data portioning, feature extraction, model training and model testing. Performance of DBNFS was demonstrated and training time and accuracy of proposed DBNFS was also compared with other algorithms. Five Dataset of sentiment classification were used for estimation, datasets are books (BOO), electronics (ELE), DVDs (DVD), kitchen appliances (KIT) and movies reviews (MOV). For fair comparison, the parameters of learning were same as of existing works. Accuracy was evaluated by comparing the amount of features before and after the feature selection and reduction. The accuracy results were compared with the previous works and were proved better DBNFS than DBN. The training time was also lower in DBNFS than DBN. Training time was improved due to simple deep structure and proposed feature selection method. The only drawback of DBN was that it is costly plus time consuming. Summarized analysis of existing approaches for sentiment analysis by using DBN is presented in Table V .