There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Social media is a powerful source of communication among people to share their sentiments in the form of opinions and views about any topic or article, which results in an enormous amount of unstructured information. Business organizations need to process and study these sentiments to investigate data and to gain business insights. Hence, to analyze these sentiments, various machine learning, and natural language processing-based approaches have been used in the past. However, deep learning-based methods are becoming very popular due to their high performance in recent times. This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis. We present a taxonomy of sentiment analysis and discuss the implications of popular deep learning architectures. The key contributions of various researchers are highlighted with the prime focus on deep learning approaches. The crucial sentiment analysis tasks are presented, and multiple languages are identified on which sentiment analysis is done. The survey also summarizes the popular datasets, key features of the datasets, deep learning model applied on them, accuracy obtained from them, and the comparison of various deep learning models. The primary purpose of this survey is to highlight the power of deep learning architectures for solving sentiment analysis problems.

Sentiment analysis using deep learning architectures: a review

Recently, the world has witnessed an exponential growth of social networks which have opened a venue for online users to express and share their opinions in different life aspects. Sentiment analysis has become a hot-trend research topic in the field of natural language processing due to its significant roles in analyzing the public’s opinion and deriving effective opinion-based decisions. Arabic is one of the widely used languages across social networks. However, its morphological complexities and varieties of dialects make it a challenging language for sentiment analysis. Therefore, inspired by the success of deep learning algorithms, in this paper, we propose a novel deep learning model for Arabic language sentiment analysis based on one layer CNN architecture for local feature extraction, and two layers LSTM to maintain long-term dependencies. The feature maps learned by CNN and LSTM are passed to SVM classifier to generate the final classification. This model is supported by FastText words embedding model. Extensive experiments carried out on a multi-domain corpus demonstrate the outstanding classification performance of this model with an accuracy of 90.75%. Furthermore, the proposed model is validated using different embedding models and classifiers. The results show that FastText skip-gram model and SVM classifier are more valuable alternatives for the Arabic sentiment analysis. The proposed model outperforms several well-established state-of-the-art approaches on relevant corpora with up to $$+\,20.71\%$$
 accuracy improvement.

Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks

In order to improve the performance of internet public sentiment analysis, a text sentiment analysis method combining Latent Dirichlet Allocation (LDA) text representation and convolutional neural network (CNN) is proposed. First, the review texts are collected from the network for preprocessing. Then, using the LDA topic model to train the latent semantic space representation (topic distribution) of the short text, and the short text feature vector representation based on the topic distribution is constructed. Finally, the CNN with gated recurrent unit (GRU) is used as a classifier. According to the input feature matrix, the GRU-CNN strengthens the relationship between words and words, text and text, so as to achieve high accurate text classification. The simulation results show that this method can effectively improve the accuracy of text sentiment classification.

Network text sentiment analysis method combining LDA text representation and GRU-CNN

The 4D trajectory is a multi-dimensional time series with plentiful spatial-temporal features and has a high degree of complexity and uncertainty. Aiming at these features of aircraft flight trajectory and the problem that it is difficult for existing trajectory prediction methods to extract spatial-temporal features from the trajectory data at the same time, we propose a novel 4D trajectory prediction hybrid architecture based on deep learning, which combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An 1D convolution is used to extract the spatial dimension feature of the trajectory, and LSTM is used to mine the temporal dimension feature of the trajectory. Hence the high-precision prediction of the 4D trajectory is realized based on the sufficient fusion of the above features. We use real Automatic Dependent Surveillance -Broadcast (ADS-B) historical trajectory data for experiments and compare the proposed method with a single LSTM model and BP model on the same data set. The experimental results show that the trajectory prediction accuracy of the CNN-LSTM hybrid model is superior to a single model. The prediction error is reduced by an average of 21.62% compared to the LSTM model and by an average of 52.45% compared to the BP model. It provides a certain reference for the trajectory prediction research and Air Traffic Management decision-making.

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A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction

Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent neural networks (RNNs) are two mainstream methods of deep learning in document and sentence modeling. In this paper, a model of capturing deep sentiment representation based on CNN and long short-term memory recurrent neural network (LSTM) is proposed. The model uses the pre-trained word vectors as input and employs CNN to gain significant local features of the text, then features are fed to two-layer LSTMs, which can extract context-dependent features and generate sentence representation for sentiment classification. We evaluate the proposed model by conducting a series of experiments on dataset. The experimental results show that the model we designed outperforms existing CNN, LSTM, CNN-LSTM (our implement of one-layer LSTM directly stacked on one-layer CNN) and SVM (support vector machine).

Xianghan Zheng

Papers

Deep Sentiment Representation Based on CNN and LSTM