Sentiment Classification Based on Part-of-Speech and Self-Attention Mechanism

Cheng, Kefei; Yue, Yanan; Song, Zhiwen

doi:10.1109/access.2020.2967103

Cited by 44 publications

(16 citation statements)

References 30 publications

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“…In this paper, for each word, we first use Google Translate, NLTK, and the German morphological analysis tool. In this paper, for each word, the lexical properties of "adjective, adverb, verb, noun, exclamation, Emoji, or other" are firstly labeled by Google Translate, NLTK, Py-Mystem1, and PyMorphy22 and then aggregated by the above four results and obtained by majority voting method [22].…”

Section: Word-level Emotional Featuresmentioning

confidence: 99%

“…German social media users often use combinations of punctuation marks to simulate facial expressions or emotion-related things and thus express positive or negative emotions, for example, "^_^" for a smiley face and "3)" for heart and love. In this paper, we refer to the literature [22,23], count the number of each type of emoticon with Table 2 as the polarity classification criterion, and use the number of emoticons of each type of polarity in a sentence as the emoticon characteristics of German texts to reflect the strength of positive and negative emotion polarity in German texts.…”

Section: Emoticon Characteristics Similar To Other Languagesmentioning

confidence: 99%

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Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

2021

Security and Communication Networks

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In this paper, we propose a multilevel feature representation method that combines word-level features, such as German morphology and slang, and sentence-level features, such as special symbols and English-translated sentiment information, and build a deep learning model for German sentiment classification based on the self-attentive mechanism, in order to address the characteristics of German social media texts that are colloquial, irregular, and diverse. Compared with the existing studies, this model not only has the most obvious improvement effect but also has better feature extraction and classification ability for German emotion.

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Section: Word-level Emotional Featuresmentioning

confidence: 99%

Section: Emoticon Characteristics Similar To Other Languagesmentioning

confidence: 99%

Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

2021

Security and Communication Networks

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“…NLP&CC2013 and NLP&CC2014 datasets were used for Weibo sentiment classification task for the International Conference on Natural Language Processing and Chinese Computing, which has been widely used in the training and evaluation of Chinese sentiment classification models in recent years [ 79 , 80 ]. Because the emergency data of this study were also from the Weibo platform, and in order to fairly evaluate the performance of the sentiment classification model used in this paper, the NLP&CC dataset was chosen as the training and test data.…”

Section: Methodsmentioning

confidence: 99%

Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

Cai

Luo

Xiao

et al. 2021

Sensors

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The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective of network, this study aims to explore the topic and emotion propagation mechanism, as well as the interaction and communication relations of the public in social networks under four types of emergencies, including public health events, accidents and disasters, social security events, and natural disasters. Event topics were identified by Word2vec and K-means clustering. The biLSTM model was used to identify emotion in posts. The propagation maps of topic and emotion were presented visually on the network, and the synergistic relationship between topic and emotion propagation as well as the communication characteristics of multiple subjects were analyzed. The results show that there were similarities and differences in the propagation mechanism of topic and emotion in different types of emergencies. There was a positive correlation between topic and emotion of different types of users in social networks in emergencies. Users with a high level of topic influence were often accompanied by a high level of emotion appeal.

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“…Focal loss adjusts the weight of the loss of easy-to-classify samples to achieve the effect of balancing sample categories. [45]…”

Section: Input Layer Feature Extraction Predictionmentioning

confidence: 99%

Sentiment Analysis of Review Text Based on BiGRU-Attention and Hybrid CNN

Zhu

Jiang

Ye³

2021

IEEE Access

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Convolutional neural networks (CNN), recurrent neural networks (RNN), attention, and their variants are extensively applied in the sentiment analysis, and the effect of fusion model is expected to be better. However, fusion model is confronted with some problems such as complicated structure, excessive trainable parameters, and long training time. The classification effect of traditional model with cross entropy loss as loss function is undesirable since sample category imbalance as well as ease and difficulty of sample classification is not taken into account. In order to solve these problems, the model BiGRU-Att-HCNN is proposed on the basis of bidirectional gated recurrent unit (BiGRU), attention, and hybrid convolutional neural networks. In this model, BiGRU and self-attention are combined to acquire global information, and key information weight is supplemented. Two parallel convolutions (dilated convolution and standard convolution) are used to obtain multi-scale characteristic information with relatively less parameters, and the standard convolution is replaced with depthwise separable convolution with two-step calculations. Traditional max-pooling and average-pooling are discarded, and global average pooling is applied to substitute the pooling layer and the fully-connected layer simultaneously, making it possible to substantially decrease the number of model parameters and reduce over-fitting. In our model, focal loss is used as the loss function to tackle the problems of unbalanced sample categories and hard samples. Experimental results illustrate that in terms of multiple indicators, our model outperforms the 15 benchmark models, even with intermediate number of trainable parameters.

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Sentiment Classification Based on Part-of-Speech and Self-Attention Mechanism

Cited by 44 publications

References 30 publications

Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

Sentiment Analysis of Review Text Based on BiGRU-Attention and Hybrid CNN

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