Sentiment Analysis Using Deep Learning Techniques: A Review <s> B. Deep learning <s> Investors have always been interested in stock price forecasting. Since the development of electronic media, hundreds pieces of financial news are released on different media every day. Numerous studies have attempted to examine whether the stock price forecasting through text mining technology and machine learning could lead to abnormal returns. However, few of them involved the discussion on whether using different media could affect forecasting results. Financial sentiment analysis is an important research area of financial technology (FinTech). This research focuses on investigating the influence of using different financial resources to investment and how to improve the accuracy of forecasting through deep learning. The experimental result shows various financial resources have significantly different effects to investors and their investments, while the accuracy of news categorization could be improved through deep learning. <s> BIB001 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> B. Deep learning <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> B. Deep learning <s> Sentiment classification has been a very hot topic in the field of natural language processing (NLP) and understanding in recent years. Recurrent neural networks (RNN) is a widely used tool to deal with the classification problem of variable-length sentences. The standard RNN can only access the preceding context of a sentence. In this paper, a new architecture termed Comprehensive Attention Recurrent Neural Networks (CA-RNN) which can store preceding, succeeding and local contexts of any position in a sequence is developed. The bidirectional recurrent neural networks (BRNN) is used to access the past and future information while a convolutional layer is employed to capture local information. The standard RNN is also replaced by two recently emerged RNN variants, namely long short-term memory (LSTM) and gated recurrent unit (GRU), to enhance the effectiveness of the new architecture. Another salient feature of the proposed model is that it can be trained end-to-end without any human intervention. It is very easy to be implemented. We conduct experiments on several sentiment-labeled datasets and analysis tasks. Experiment results demonstrate that capturing comprehensive contextual information can significantly enhance the classification accuracy compared with the standard recurrent models and the new models can achieve competitive performance compared with the state-of-the-art approaches. <s> BIB003
Deep Learning was firstly proposed by G.E. Hinton in 2006 and is the part of machine learning process which refers to Deep Neural Network BIB001 . Neural network is influenced by human brain and it contains several neurons that make an impressive network. Deep learning networks are capable for providing training to both supervised and unsupervised categories BIB002 . Deep learning includes many networks such as CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), Recursive Neural Networks, DBN (Deep Belief Networks) and many more. Neural networks are very beneficial in text generation, vector representation, word representation estimation, sentence classification, sentence modeling and feature presentation. BIB003 .