Sentiment Analysis Using Deep Learning Techniques: A Review <s> F. Hybrid Neural Networks <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> F. Hybrid Neural Networks <s> Sentiment analysis is very important for social listening, especially, when there are millions of Twitter users in Thailand nowadays. Almost all prior works are based on classical classification techniques, e.g., SVM, Naive Bayes, etc. Recently, the deep learning techniques have shown promising accuracy in this domain on English tweet corpus. In this paper, we propose the first study that applies deep learning techniques to classify sentiment of Thai Twitter data. There are two deep learning techniques included in our study: Long Short Term Memory (LSTM) and Dynamic Convolutional Neural Network (DCNN). A proper data preprocessing has been conducted. Moreover, we also investigate an effect of word orders in Thai tweets. The results show that the deep learning techniques significantly outperform many classical techniques: Naive Bayes and SVM, except Maximum Entropy. <s> BIB002 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> F. Hybrid Neural Networks <s> Evolution of plethora of e-commerce sites resulted in fierce competition among their providers. In order to acquire new and retain existing customers, various producers and market managers effectively employ online feedback analytics tools. Most of the online feedback analysis tools are built using sentiment analysis models. Sentiment analysis evolved in the last one and half decades for review mining process. An important sub-task of sentiment analysis called sentiment classification is used mainly to decide whether a written review is expressing either positive or negative sentiment towards a target entity. In order to have better sentiment classification accuracy, we proposed a hybrid deep learning architecture, which is a hybrid of a two layered Restricted Boltzmann Machine and a Probabilistic Neural Network. The proposed approach yielded better accuracy for five different datasets compared to the state-of-the-art. <s> BIB003
This study BIB002 has proposed two deep learning techniques for the sentiment classification of Thai Twitter data, i.e., Convolutional Neural Network (DCNN) and Short Term Memory (LSTM). Data processing was conducted properly. Data was collected from the users and their followers of Thai Twitter. After filtering the data, only the users with Thai tweets and tweets with Thai characters were selected. Five experiments were conducted to achieve finest parameters for deep learning, to compare the deep learning with classical techniques and to achieve the words sequence importance. Three-fold cross validation was used to verify the process. The results concluded that the accuracy is high in DNN than LSTM and both techniques of deep learning are higher in accuracy than SVM and Nave Bayes but lesser than Maximum Entropy. Higher accuracies were found in original sentences than shuffled sentences so the words sequence is important. In this research study BIB003 a hybrid model has proposed which consists of Probabilistic Neural Network (PNN) and a two layered Restricted Boltzmann (RBM). The purpose of proposing this hybrid deep learning model is to attain better accuracy of sentiment classification. The polarity, i.e., negative and positive reviews vary according to different context in order to solve this type of problem this model performs well, neutral reviews are not considered. Experiments have done with datasets of Pang and Lee and Blitzer, et al., binary classification implemented on every dataset. The accuracy has been enhanced for five datasets by comparing with the existing state-of-the-art Dang, et al. BIB001 . There are no outer resources in proposed approach such as POS tagger and sentiment dictionary etc therefore it is faster too than competitor. To attain a reduced number of features the dimensionality reduction has been implemented as previous study used a complex strategy for feature selection. Approaches based on hybrid neural networks are presented in summarized form in Table  VI . The accuracy results are compared with previous works and proved better DBNFS than DBN.