Targeted sentiment classification aims to predict the emotional trend of a specific goal. Currently, most methods (e.g., recurrent neural networks and convolutional neural networks combined with an attention mechanism) are not able to fully capture the semantic information of the context and they also lack a mechanism to explain the relevant syntactical constraints and long-range word dependencies. Therefore, syntactically irrelevant context words may mistakenly be recognized as clues to predict the target sentiment. To tackle these problems, this paper considers that the semantic information, syntactic information, and their interaction information are very crucial to targeted sentiment analysis, and propose an attentional-encoding-based graph convolutional network (AEGCN) model. Our proposed model is mainly composed of multi-head attention and an improved graph convolutional network built over the dependency tree of a sentence. Pre-trained BERT is applied to this task, and new state-of-art performance is achieved. Experiments on five datasets show the effectiveness of the model proposed in this paper compared with a series of the latest models.
In the human-computer interaction (HCI), electroencephalogram (EEG) access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hybrid network was proposed in this paper. Firstly, the collected EEG was decomposed into four frequency band signals, and the multiscale sample entropy (MSE) features of each frequency band were extracted. Secondly, the constructed 3D MSE feature matrices were fed into a deep hybrid network for autonomous learning. The deep hybrid network was composed of a continuous convolutional neural network (CNN) and hidden Markov models (HMMs). Lastly, HMMs trained with multiple observation sequences were used to replace the artificial neural network classifier in the CNN, and the emotion recognition task was completed by HMM classifiers. The proposed method was applied to the DEAP dataset for emotion recognition experiments, and the average accuracy could achieve 79.77% on arousal, 83.09% on valence, and 81.83% on dominance. Compared with the latest related methods, the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method.
Sentiment word embedding has been extensively studied and used in sentiment analysis tasks. However, most existing models have failed to differentiate high-frequency and lowfrequency words. Accordingly, the sentiment information of low-frequency words is insufficiently captured, thus resulting in inaccurate sentiment word embedding and degradation of overall performance of sentiment analysis. A Bayesian estimation-based sentiment word embedding (BESWE) model, which aims to precisely extract the sentiment information of low-frequency words, has been proposed. In the model, a Bayesian estimator is constructed based on the co-occurrence probabilities and sentiment probabilities of words, and a novel loss function is defined for sentiment word embedding learning. The experimental results based on the sentiment lexicons and Movie Review dataset show that BESWE outperforms many state-of-the-art methods, for example, C&W, CBOW, GloVe, SE-HyRank and DLJT1, in sentiment analysis tasks, which demonstrate that Bayesian estimation can effectively capture the sentiment information of low-frequency words and integrate the sentiment information into the word embedding through the loss function. In addition, replacing the embedding of low-frequency words in the state-of-the-art methods with BESWE can significantly improve the performance of those methods in sentiment analysis tasks.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The most commonly used methods in text sentiment analysis are rule-based sentiment dictionary and machine learning, with the later referring to the use of vectors to represent text followed by the use of machine learning to classify the vectors. Both methods have their limitations, including inflexibility of rules, non-prominence of sentiment words. In this paper, we design a weight distributing method combining the two methods for text sentiment analysis, by which the sentence vectors obtained can both highlight words with sentiment meanings while retaining their text information. Empirical results show that based on this new method, the accuracy rate of text sentiment analysis can reach as high as 82.1%, which means 13.9% higher than rule-based sentiment dictionary method, and 7.7% higher than TF-IDF weighting method.
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