Cognitive Detection of Multiple Discrete Emotions from Chinese Online Reviews

Jiang, Si; Qi, Jiayin

doi:10.1109/dsc.2016.85

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…In particular, the OCC theory contains detailed discrete emotions in a context concerning the different consequences of events, actions of agents, and aspects of objects, and OCC theory is widely used as a computational model to depict the human cognition process. It identifies the underlying cognitive structure and emotional dimensions [ 28 ]. This approach argues that emotional reactions to an event are a product of personal interpretations and appraisals in a situational environment [ 29 , 30 ].…”

Section: Methodsmentioning

confidence: 99%

Perceiving Social-Emotional Volatility and Triggered Causes of COVID-19

Jiang

Zhang

et al. 2021

IJERPH

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Health support has been sought by the public from online social media after the outbreak of novel coronavirus disease 2019 (COVID-19). In addition to the physical symptoms caused by the virus, there are adverse impacts on psychological responses. Therefore, precisely capturing the public emotions becomes crucial to providing adequate support. By constructing a domain-specific COVID-19 public health emergency discrete emotion lexicon, we utilized one million COVID-19 theme texts from the Chinese online social platform Weibo to analyze social-emotional volatility. Based on computed emotional valence, we proposed a public emotional perception model that achieves: (1) targeting of public emotion abrupt time points using an LSTM-based attention encoder-decoder (LAED) mechanism for emotional time-series, and (2) backtracking of specific triggered causes of abnormal volatility in a cognitive emotional arousal path. Experimental results prove that our model provides a solid research basis for enhancing social-emotional security outcomes.

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Section: Methodsmentioning

confidence: 99%

Perceiving Social-Emotional Volatility and Triggered Causes of COVID-19

Jiang

Zhang

et al. 2021

IJERPH

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“…An accuracy of 80 to 85% is achieved as compared to the baseline method. Jiang and Qi (2016) presented a chines emotion detection system for classifying user's emotions from online product reviews. For this purpose, an enhanced OCC-OR emotion model is used by selecting six emotion categories.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

Asghar¹,

Subhan²,

Imran³

et al. 2019

Preprint

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Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

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An Atypical Approach for Uncovering the Essence of Emotions in Consumer Reviews

Dhage¹,

Shama²

2018

2018 4th International Conference on Computer and Information Sciences (ICCOINS)

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Cognitive Detection of Multiple Discrete Emotions from Chinese Online Reviews

Cited by 3 publications

References 34 publications

Perceiving Social-Emotional Volatility and Triggered Causes of COVID-19

Perceiving Social-Emotional Volatility and Triggered Causes of COVID-19

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

An Atypical Approach for Uncovering the Essence of Emotions in Consumer Reviews

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