Sentiment Analysis Using Deep Learning Techniques: A Review <s> C. Combining Sentiment Analysis and Deep Learning <s> Recursive structure is commonly found in the inputs of different modalities such as natural scene images or natural language sentences. Discovering this recursive structure helps us to not only identify the units that an image or sentence contains but also how they interact to form a whole. We introduce a max-margin structure prediction architecture based on recursive neural networks that can successfully recover such structure both in complex scene images as well as sentences. The same algorithm can be used both to provide a competitive syntactic parser for natural language sentences from the Penn Treebank and to outperform alternative approaches for semantic scene segmentation, annotation and classification. For segmentation and annotation our algorithm obtains a new level of state-of-the-art performance on the Stanford background dataset (78.1%). The features from the image parse tree outperform Gist descriptors for scene classification by 4%. <s> BIB001 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> C. Combining Sentiment Analysis and Deep Learning <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> BIB002 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> C. Combining Sentiment Analysis and Deep Learning <s> The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. ::: ::: An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. <s> BIB003 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> C. Combining Sentiment Analysis and Deep Learning <s> Although temporal information of speech has been shown to play an important role in perception, most of the voice conversion approaches assume the speech frames are independent of each other, thereby ignoring the temporal information. In this study, we improve conventional unit selection approach by using exemplars which span multiple frames as base units, and also take temporal information constraint into voice conversion by using overlapping frames to generate speech parameters. This approach thus provides more stable concatenation cost and avoids discontinuity problem in conventional unit selection approach. The proposed method also keeps away from the over-smoothing problem in the mainstream joint density Gaussian mixture model (JD-GMM) based conversion method by directly using target speaker’s training data for synthesizing the converted speech. Both objective and subjective evaluations indicate that our proposed method outperforms JD-GMM and conventional unit selection methods. Index Terms: Voice conversion, unit selection, multi-frame exemplar, temporal information <s> BIB004 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> C. Combining Sentiment Analysis and Deep Learning <s> The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline. <s> BIB005 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> C. Combining Sentiment Analysis and Deep Learning <s> Sentiment analysis of text content is important for many natural language processing tasks. Especially, as the development of the social media, there is a big need in dig meaningful information from the big data on Internet through the sentiment analysis. Inspired by the successes of deep learning, we are interested in handling the sentiment analysis task using deep learning models. In this paper, we propose a framework called Word2vec + Convolutional Neural Network (CNN). Firstly, we use the word2vec proposed by Google to compute vector representations of words, which will be the input for the CNN. The purpose of using word2vec is to gain the vector representation of word and reflect the distance of words. That will lead to initialize the parameters at a good point of CNN, which can efficiently improve the performance of the nets in this problem. Secondly, we design a suitable CNN architecture for the sentiment analysis task. We use 3 pairs of convolutional layers and pooling layers in this architecture. To the best of our knowledge, this is the first time that a 7-layers architecture model is applied using word2vec and CNN to analyze sentences' sentiment. We also use the Parametric Rectified Linear Unit (PReLU), Normalization and Dropout technology to improve the accuracy and generalizability of our model. We test our framework in a public dataset which is the corpus of movie review excerpts that includes fives labels: negative, somewhat negative, neural, somewhat positive and positive. Our network achieves test accuracy of 45.4% in this dataset, which is a better performance than some other neural network model like Recursive Neural Network (RNN) and Matrix-Vector Recursive Neural Network (MV-RNN). <s> BIB006 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> C. Combining Sentiment Analysis and Deep Learning <s> Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. Tree-LSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank). <s> BIB007
Deep learning is very influential in both unsupervised and supervised learning, many researchers are handling sentiment analysis by using deep learning. It consists of numerous effective and popular models and these models are used to solve the variety of problems effectively BIB006 . The most famous example Socher has used is the Recursive Neural Network (RNN) for the representation of movies reviews from the website rottentomatoes.com BIB001 . By following the effort of BIB002 , many researchers has performed sentiment classification by using neural networks. For example, Kalchbrenner BIB005 anticipated a DyCNN (Dynamic Convolutional Neural Network) which use a pooling operation, i.e, dynamic k-max pooling on linear sequences. Kim  use CNN to learn sentiment-bearing sentence vectors. Furthermore, Paragraph vector is proposed by Mikolov, et al. BIB003 which performs better sentiment analysis than bagof-words model and ConvNets for learning the SSWE (sentiment specific word embedding). Recently BIB004 has applied ConvNets on characters rather than directly applying on word embeddings. Another composition model for the classification of sentiments is LSTM which can simply take flow on one input direction BIB007 . In this paper, Section II covers the detailed literature review and Section III presents the conclusion whereas, Section IV shows the analysis phase been analyzed in literature review. In the Literature Review section, various studies of sentiment analysis using Deep Learning techniques are discussed. This review is conducted on the basis of numerous latest studies in the field of sentiment analysis.