Emotion Analysis in Code-Switching Text With Joint Factor Graph Model

Affective analysis of social media text is in great demand. Online text written in Chinese communities often contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and minor text using Latin letters, an alphabet-based writing system. This phenomenon is referred to as writing systems changes (WSCs). Past studies have shown that WSCs often reflect unfiltered immediate affections. However, the use of WSCs poses more challenges in Natural Language Processing tasks because WSCs can break the syntax of the major text. In this work, we present our work to use WSCs as an effective feature in a hybrid deep learning model with attention network. The WSCs scripts are first identified by their encoding range. Then, the document representation of the text is learned through a Long Short-Term Memory model and the minor text is learned by a separate Convolution Neural Network model. To further highlight the WSCs components, an attention mechanism is adopted to re-weight the feature vector before the classification layer. Experiments show that the proposed hybrid deep learning method which better incorporates WSCs features can further improve performance compared to the state-of-the-art classification models. The experimental result indicates that WSCs can serve as effective information in affective analysis of the social media text.

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“…WSCs have been recognized to be relevant to affections [20,21]. These statistical studies show that WSCs frequently occur in social media.…”

Section: Related Workmentioning

confidence: 92%

Leveraging writing systems changes for deep learning based Chinese affective analysis

Xiang

Jiao

et al. 2019

Int. J. Mach. Learn. & Cyber.

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“…Current methods of emotion recognition mainly involve facial expression recognition [3][4][5][6], speech emotion recognition [7][8][9], gesture expression recognition [10], text recognition [11], physiological pattern recognition, and multimodal emotion recognition [12][13][14][15]. In practical applications, the non-contact method of extracting physiological parameters for face imaging has attracted special attention.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial–Spectral–Temporal Framework for Emotion Recognition

Hong

2020

IEEE Access

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An emotion recognition method based on multispectral imaging technology and tissue oxygen saturation (StO2) is proposed in this study. This method is called spatial-spectral-temporal adjustment convolutional neural network (SACNN). First, we use the algorithm to extract the StO2 content of an emotionally sensitive nose area through real-time multispectral imaging technology. Compared with facial expression data, StO2 data are more objective and cannot be controlled and changed artificially. Second, we construct a clustering algorithm based on the emotional state by extracting the spectral, StO2, and spatial features of the nose image to obtain accurate signals of emotionally sensitive areas. To utilize the correlation between spectral and spatial signals, we propose an adjustment-based CNN module, which reorganizes the relationship between all previous layers of the feature map, thereby making the relationship among layers close and highly quantitative. The features extracted through this method are consistent with spatial-spectral features. Third, we incorporate the extracted temporal feature signal into the long short-term memory module and finally complete the correlation between the spatial-spectral-temporal features. Experimental results show that the accuracy of the SACNN algorithm in emotional recognition reaches 90%, and the proposed method is more competitive than state-of-the-art approaches. To the best of our knowledge, this study is the first to use time-series StO2 signals for emotion recognition. INDEX TERMS Multispectral imaging, oxygen saturation, spatial-spectral-temporal adjustment convolutional neural network. I.

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“…The annotation guideline is based on Lee et al (2013). Five basic emotions are annotated, namely happiness, sadness, fear, anger and surprise (Lee et al, 2013;Wang et al, 2017). Table 1 illustrates the statistics of each emotion.…”

Section: Experimental Settingsmentioning

confidence: 99%

Emotion Detection with Neural Personal Discrimination

Zhou

Wang

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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There have been a recent line of works to automatically predict the emotions of posts in social media. Existing approaches consider the posts individually and predict their emotions independently. Different from previous researches, we explore the dependence among relevant posts via the authors' backgrounds, since the authors with similar backgrounds, e.g., gender, location, tend to express similar emotions. However, such personal attributes are not easy to obtain in most social media websites, and it is hard to capture attributesaware words to connect similar people. Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions. In particular, we employ adversarial discriminators to determine the personal attributes, with attention mechanisms to aggregate attributes-aware words. In this way, social correlationship among different posts can be better addressed. Experimental results show the usefulness of personal attributes, and the effectiveness of our proposed NPD approach in capturing such personal attributes with significant gains over the state-of-the-art models.

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Emotion Analysis in Code-Switching Text With Joint Factor Graph Model

Cited by 25 publications

References 59 publications

Leveraging writing systems changes for deep learning based Chinese affective analysis

Leveraging writing systems changes for deep learning based Chinese affective analysis

Spatial–Spectral–Temporal Framework for Emotion Recognition

Emotion Detection with Neural Personal Discrimination

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