Sentiment Analysis Using Deep Learning Techniques: A Review <s> A. Convolutional Neural Networks (CNN) <s> This paper describes our deep learning system for sentiment analysis of tweets. The main contribution of this work is a new model for initializing the parameter weights of the convolutional neural network, which is crucial to train an accurate model while avoiding the need to inject any additional features. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning model on a distant supervised corpus. At a final stage, the pre-trained parameters of the network are used to initialize the model. We train the latter on the supervised training data recently made available by the official system evaluation campaign on Twitter Sentiment Analysis organized by Semeval-2015. A comparison between the results of our approach and the systems participating in the challenge on the official test sets, suggests that our model could be ranked in the first two positions in both the phrase-level subtask A (among 11 teams) and on the message-level subtask B (among 40 teams). This is an important evidence on the practical value of our solution. <s> BIB001 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> A. Convolutional Neural Networks (CNN) <s> Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using additional images and videos to express their opinions and share their experiences. Sentiment analysis of such large-scale textual and visual content can help better extract user sentiments toward events or topics. Motivated by the needs to leverage large-scale social multimedia content for sentiment analysis, we utilize both the state-of-the-art visual and textual sentiment analysis techniques for joint visual-textual sentiment analysis. We first fine-tune a convolutional neural network (CNN) for image sentiment analysis and train a paragraph vector model for textual sentiment analysis. We have conducted extensive experiments on both machine weakly labeled and manually labeled image tweets. The results show that joint visual-textual features can achieve the state-of-the-art performance than textual and visual sentiment analysis algorithms alone. <s> BIB002 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> A. Convolutional Neural Networks (CNN) <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> BIB003 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> A. Convolutional Neural Networks (CNN) <s> Visual sentiment analysis framework can predictthe sentiment of an image by analyzing the image contents. Nowadays, people are uploading millions of images in socialnetworks such as Twitter, Facebook, Google Plus, and Flickr. These images play a crucial part in expressing emotions of users in online social networks. As a result, image sentiment analysishas become important in the area of online multimedia big dataresearch. Several research works are focusing on analyzing thesentiment of the textual contents. However, little investigationhas been done to develop models that can predict sentiment ofvisual content. In this paper, we propose a novel visual sentimentanalysis framework using transfer learning approach to predictsentiment. We use hyper-parameters learned from a very deepconvolutional neural network to initialize our network modelto prevent overfitting. We conduct extensive experiments on aTwitter image dataset and prove that our model achieves betterperformance than the current state-of-the-art. <s> BIB004
The CNN (convolutional neural network)  includes pooling layers and sophistication as it gives a standard architecture to map the sentences of variable length into sentences of fixed size scattered vectors. This study BIB004 has proposed a novel convolutional neural network (CNN) framework for visual sentiment analysis to predict sentiments of visual content. CNN has been implemented using Caffe and Python on a Linux machine. Transfer learning approach and hyper-parameter has been used in biases and weights are utilized from pre-trained GoogLeNet. As CNN enhance its performance by increasing its size and depth, so a very deep CNN model, inspired by GoogLeNet is proposed with 22 layers for sentiment analysis. It is optimized by using SGD (Stochastic gradient descent) algorithm. The strategy with 60 epochs has been performed for training the network as GoogLeNet has performed 250 epochs. For experimental work, a dataset of twitter containing 1269 images is selected and back propagation is applied. Amazon Mechanical Turk (MTurk) and popular crowd intelligence is used to label the images. Five workers were involved to generate sentiment label in favor of every image. The proposed model was evaluated on this dataset and acquired better performance than existing systems. Results shows that proposed system achieve high performance without fine-tuning on Flickr dataset. However AlexNet was used in previous works and GoogleNet provided almost 9% performance progress than AlexNet. By converting GoogLeNet in to visual sentiment analysis framework, the better feature extraction was achieved. Stable and reliable state were achieved by using hyper parameters. The authors BIB001 have proposed the system of deep learning for sentiment analysis of twitter. The main focus of this work was to initialize the weight of parameters of convolutional neural network and it is critical to train the model accurately while avoiding the requirement of adding new feature. A neural language is used to initialize the word embedding and is trained by big unsupervised group of tweets. For further refining the embedding on bulky supervised corpus, a conventional neural network is used. To initialize the network, previously embedded words and parameters were used, having same architecture and training on supervised corpus as of Semeval-2015. The components used in proposed work are activations, sentence matrix pooling, softmax and convolutional layers. To train the network, stochastic gradient descent (SGD) and non-convex function optimization algorithms were used and to calculate the gradients back propagation algorithm was used. Dropout technique were used to enhance the neural networks regularization. The deep learning model is applied on two tasks: message level task and phrase level task from Semeval-2015 to predict polarity and achieve high outcomes. By applying six test-set, the proposed model lies at first rank in terms of accuracy. A detailed research by  has presented an overview of sentiment analysis related to Micro-blog. The purpose of this effort was to get the opinions and attitudes of users about hot events by using Convolutional Neural Network (CNN). The use of CNN overcomes the problem of explicit feature extraction and learns implicitly through training data. To collect the data from target, the input URL and focused crawler have been used, 1000 micro-blog comments were collected as a corpus and divided into three labels, i.e., 274 neutral emotions, 300 negative emotions and 426 positive emotions. The proposed model has been compared with the previous studies as those had studies used CRF, SVM and additional traditional algorithms to perform sentiment analysis with a high price. However, the performance proves that the proposed model is reasonable and sufficient to enhance the accuracy in terms of emotion analysis. Research by BIB002 was motivated through the need of controlling of comprehensive social multimedia content and employ both textual and visual SA techniques for combined textual-visual sentiment analysis. A convolutional neural network (CNN) and a paragraph vector model were used for both the image and textual SA accordingly. The proposed model was termed as rule based sentiment classifier VADER. After conducting the wide range of experiments on manually labeled and weakly labeled visual tweets, it was concluded that mutual textual-visual features outperformed the sentiment analysis algorithms which were only depend on Visual contents. It was demonstrated that how models of one domain to a different domain can be transmitted easily. Getty Images had been selected to crawl data and Caffe was used to tune the CNN model. Tweets were gathered through Twitter API. To make sentiment labels for chosen visual tweets, the Mechanical Turk (AMT) and crowd intelligence had been employed. The results recommend that the joint textual-visual model has performed better than the both single visual and textual sentiment analysis models. In study by BIB003 , the researcher has represented a sevenlayer framework to analyze the sentiments of sentences. This frame work depends on CNN (Convolutional neural network) and Word2vec for SA and to calculate vector representation, respectively. Word2vec have been proposed by Google. The Dropout technology, Normalization and Parametric Rectified Linear Unit (PReLU), have been used to progress the correctness and generalizability of proposed model. The framework was verified on the data set from rottentomatoes.com which contains movie review excerpts' corpus, the dataset consists of five labels positive, somewhat positive, neural, negative and somewhat negative. By comparing the proposed model with previous models such as Matrix-Vector recursive neural network (MV-RNN) and recursive neural network (RNN), the proposed model outperformed the previous models with the 45.5 % accuracy. In Table I , we have presented the analysis of best studies following the convolutional neural network.