CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs

Gonzalez, Ana Valeria; Hansen, Victor Petrén Bach; Bingel, Joachim; Søgaard, Anders

doi:10.18653/v1/s19-2026

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The proposed model have been evaluated through two different datasets namely semeval 2019 task3 dataset [15] and ISEAR dataset [13]. Semeval 2019 dataset contains four classes of emotions: angry, happy, sad, and others.…”

Section: Datasetmentioning

confidence: 99%

“…The BERT-CNN performance is compared to performances of the state-of-the-art models using two datasets mentioned in the previous sections. These models are Emotdet [16], EMODET 2 [14], Nture [17], SCIA [18], Coastal [15], PKUSE [19], EPITA-ADAPT [20], Figure Eight [21], NELEC [22], THU NGN [23], LIRMM [24], NTUA-ISLab [25], Syman to Research [26], ANA [27], CAiRE-HKUST [28], GenSMT [29], SNU_IDS [30], CLARK [31], and SINAI [32].…”

Section: Compared Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

Abas¹,

El-Henawy²,

Zidan³

et al. 2022

Computers, Materials &Amp; Continua

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Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition and Natural Language Processing (NLP). In this field, users' feedback data on a specific issue are evaluated and analyzed. Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text. This model is formed by a combination of the Bidirectional Encoder Representations from Transformer (BERT) and the Convolutional Neural networks (CNN) for textual classification. This model embraces the BERT to train the word semantic representation language model. According to the word context, the semantic vector is dynamically generated and then placed into the CNN to predict the output. Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets. The BERT-CNN model achieves an accuracy of 94.7% and an F1-score of 94% for semeval2019 task3 dataset and an accuracy of 75.8% and an F1-score of 76% for ISEAR dataset.

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Section: Datasetmentioning

confidence: 99%

Section: Compared Algorithmsmentioning

confidence: 99%

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

Abas¹,

El-Henawy²,

Zidan³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Ma et al [43] proposed an attentive LSTM for aspect-based sentiment analysis. González et al [44] proposed an attentive BiLSTM for affect classification in dialogue. Kumar et al [45] proposed a soft attention-based LSTM with CNN for sarcasm detection.…”

Section: Related Workmentioning

confidence: 99%

Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

et al. 2020

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Convolutional Neural Network(CNN) and Recurrent Neural Network(RNN) have been widely used in the field of text sentiment analysis and have achieved good results. However, there is an anteroposterior dependency between texts, although CNN can extract local information between consecutive words of a sentence, it ignores the contextual semantic information between words. Bidirectional GRU can make up for the shortcomings that CNN can't extract contextual semantic information of long text, but it can't extract the local features of the text as well as CNN. Therefore, we propose a multi-channel model that combines the CNN and the bidirectional gated recurrent unit network with attention mechanism (MC-AttCNN-AttBiGRU). The model can pay attention to the words that are important to the sentiment polarity classification in the sentence through the attention mechanism and combine the advantages of CNN to extract local features of text and bidirectional GRU to extract contextual semantic information of long text, which improves the text feature extraction ability of the model. The experimental results on the IMDB dataset and Yelp 2015 dataset show that the proposed model can extract more rich text features than other baseline models, and can achieve better results than other baseline models. INDEX TERMS Convolutional Neural Network, Bidirectional gated recurrent unit network, attention mechanism, text sentiment orientation analysis.

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Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

Majumder²,

et al. 2019

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Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data in platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration) and more. Additionally, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a strenuous problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on the recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.

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CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs

Cited by 3 publications

References 16 publications

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

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