Fine-grained emotion classification of Chinese microblogs based on graph convolution networks

Lai, Yuni; Zhang, Linfeng; Han, Deqiang; Zhou, Rui; Wang, Guoren

doi:10.1007/s11280-020-00803-0

Cited by 55 publications

(25 citation statements)

References 28 publications

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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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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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“…A multi-label with multi-target emotion detection of Arabic tweets accomplished using decision trees, random forest, and KNN, where random forest provided the highest f 1 -score of 82.6% (Alzu'bi et al, 2019). Lai et al (2020) proposed a graph convolution network architecture for emotion classification from Chinese microblogs and their proposed system achieved an F-measure of 82.32%. Recently, few works employed transformer-based model (i.e., BERT) analyse emotion in texts.…”

Section: Related Workmentioning

confidence: 99%

Emotion Classification in a Resource Constrained Language Using Transformer-based Approach

Das¹,

Sharif²,

Hoque³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Rese

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Although research on emotion classification has significantly progressed in highresource languages, it is still infancy for resource-constrained languages like Bengali. However, unavailability of necessary language processing tools and deficiency of benchmark corpora makes the emotion classification task in Bengali more challenging and complicated. This work proposes a transformer-based technique to classify the Bengali text into one of the six basic emotions: anger, fear, disgust, sadness, joy, and surprise. A Bengali emotion corpus consists of 6243 texts is developed for the classification task. Experimentation carried out using various machine learning (LR, RF, MNB, SVM), deep neural networks (CNN, BiLSTM, CNN+BiLSTM) and transformer (Bangla-BERT, m-BERT, XLM-R) based approaches. Experimental outcomes indicate that XLM-R outdoes all other techniques by achieving the highest weighted f 1 -score of 69.73% on the test data. The dataset is publicly available at https://github.com/omar-sharif03/ NAACL-SRW-2021.

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“…Feng et al [11] address the group-based emotion detection exploiting topic exploration. Lai et al [20] adopt graph convolutional networks to enhance fine-grained emotion classification performance. However, semantic feature is not sufficient in a domain-specific task due to the data sparsity issue [19].…”

Section: Related Workmentioning

confidence: 99%

“…Disdainful fear [7] Fearful [8] Terrified [9] Anguished joy [10] Joy [11] Leisurely [12] Enjoyment [13] Free [14] Lucky [15] Love sadness [16] Sad [17] Upset [18] Depressed shame [19] Shame [20] Humiliated [21] Embarrassed guilt [22] Guilty [23] Regretful [24] Sinful module, that is, whether the propose self-attention module can still perform as expected if without semantic features or pre-trained word embedding. The above mentioned questions will be addressed one-by-one in the following.…”

Section: Labelsmentioning

confidence: 99%

EmoChannel-SA: exploring emotional dependency towards classification task with self-attention mechanism

Li¹,

Chen²,

Xie³

et al. 2021

World Wide Web

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Exploiting hand-crafted lexicon knowledge to enhance emotional or sentimental features at word-level has become a widely adopted method in emotion-relevant classification studies. However, few attempts have been made to explore the emotion construction in the classification task, which provides insights to how a sentence’s emotion is constructed. The major challenge of exploring emotion construction is that the current studies assume the dataset labels as relatively independent emotions, which overlooks the connections among different emotions. This work aims to understand the coarse-grained emotion construction and their dependency by incorporating fine-grained emotions from domain knowledge. Incorporating domain knowledge and dimensional sentiment lexicons, our previous work proposes a novel method named EmoChannel to capture the intensity variation of a particular emotion in time series. We utilize the resultant knowledge of 151 available fine-grained emotions to comprise the representation of sentence-level emotion construction. Furthermore, this work explicitly employs a self-attention module to extract the dependency relationship within all emotions and propose EmoChannel-SA Network to enhance emotion classification performance. We conducted experiments to demonstrate that the proposed method produces competitive performances against the state-of-the-art baselines on both multi-class datasets and sentiment analysis datasets.

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Fine-grained emotion classification of Chinese microblogs based on graph convolution networks

Cited by 55 publications

References 28 publications

Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

Emotion Classification in a Resource Constrained Language Using Transformer-based Approach

EmoChannel-SA: exploring emotional dependency towards classification task with self-attention mechanism

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