A Survey of Sentiment Analysis Based on Transfer Learning <s> B. SENTIMENT ANALYSIS BASED ON 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> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> B. SENTIMENT ANALYSIS BASED ON 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> BIB002 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> B. SENTIMENT ANALYSIS BASED ON DEEP LEARNING <s> Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan's economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called "Beyond AI". Specifically, we plan to develop an intelligent learning model called "Brain Intelligence (BI)" that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots. <s> BIB003 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> B. SENTIMENT ANALYSIS BASED ON DEEP LEARNING <s> Image completion has always been an important research area of image processing. With the continuous development of the deep learning model in recent years, further progress has been made in the repair of images. In this paper, we focused on realistic and painting portrait data, studied on semantic inpainting techniques based on regional completions, and proposed an improved generative translation model. Through the context generation network and the image discriminator network, a patch image is generated which should keep consistency between the hole and the surrounding area. Then the completed part will be processed according to the scene structure of the image through the style translation network to ensure the consistency between the generated area and the whole image, which means the repair part can better adapt to the style, texture, and structure of the artistic work. Experiments have shown that our method could achieve good results in the completion of realistic and painting portraits, and it also provided some reference for restoration and identification of art works. <s> BIB004 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> B. SENTIMENT ANALYSIS BASED ON DEEP LEARNING <s> Unmanned aerial vehicles (UAVs) are used in many fields including weather observation, farming, infrastructure inspection, and monitoring of disaster areas. However, the currently available UAVs are prone to crashing. The goal of this paper is the development of an anomaly detection system to prevent the motor of the drone from operating at abnormal temperatures. In this anomaly detection system, the temperature of the motor is recorded using DS18B20 sensors. Then, using reinforcement learning, the motor is judged to be operating abnormally by a Raspberry Pi processing unit. A specially built user interface allows the activity of the Raspberry Pi to be tracked on a Tablet for observation purposes. The proposed system provides the ability to land a drone when the motor temperature exceeds an automatically generated threshold. The experimental results confirm that the proposed system can safely control the drone using information obtained from temperature sensors attached to the motor. <s> BIB005 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> B. SENTIMENT ANALYSIS BASED ON DEEP LEARNING <s> The scientific and technological revolution of the Internet of Things has begun in the area of oceanography. Historically, humans have observed the ocean from an external viewpoint in order to study it. In recent years, however, changes have occurred in the ocean, and laboratories have been built on the sea floor. Approximately 70.8 percent of the Earth's surface is covered by oceans and rivers. The Ocean of Things is expected to be important for disaster prevention, ocean resource exploration, and underwater environmental monitoring. Unlike traditional wireless sensor networks, the Ocean Network has its own unique features, such as low reliability and narrow bandwidth. These features will be great challenges for the Ocean Network. Furthermore, the integration of the ocean network with artificial intelligence has become a topic of increasing interest for oceanology researchers. The cognitive ocean network (CONet) will become the mainstream of future ocean science and engineering developments. In this article, we define the CONet. The contributions of the article are as follows: a CONet architecture is proposed and described in detail; important and useful demonstration applications of the CONet are proposed; and future trends in CONet research are presented. <s> BIB006
Deep learning plays an important role in many fields such as Artificial Intelligence BIB003 , Computer Vision BIB004 and Internet of Things BIB006 , BIB005 . With the great progress in deep learning in the field of NLP, the application of deep learning to sentiment analysis has become more popular. The word representations of text is important for sentiment analysis, Bag of Words (BoW) and Word Embedding are models that commonly be used. The typical representative of the word embedding model is Word2Vec BIB001 , BIB002 . In this paper, the neural network model for sentiment analysis is divided into three types that include CNN-based models, RNN-based models and hybrid neural network models to summarize recent related researches.