Exploiting Multi-Emotion Relations at Feature and Label Levels for Emotion Tagging

Xu, Zhiwei; Wang, Shangfei; Wang, Can

doi:10.1145/3394171.3413506

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…However, it will be interesting to see the framework's performance on some previously available datasets with the same class labels. There are very limited such datasets publically available with the same class labeling as utilized in the present work [54,55]. For this experiment, Enron8715 and Emotion616 datasets are obtained as mentioned in [32].…”

Section: Experiments With Additional Datamentioning

confidence: 99%

Non-Acted Text and Keystrokes Database and Learning Methods to Recognize Emotions

Tahir

Halim

Rahman

et al. 2022

ACM Trans. Multimedia Comput. Commun. Appl.

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The modern computing applications are presently adapting to the convenient availability of huge and diverse data for making their pattern recognition methods smarter. Identification of dominant emotion solely based on the text data generated by humans is essential for the modern human–computer interaction. This work presents a multimodal text-keystrokes dataset and associated learning methods for the identification of human emotions hidden in small text. For this, a text-keystrokes data of 69 participants is collected in multiple scenarios. Stimuli are induced through videos in a controlled environment. After the stimuli induction, participants write their reviews about the given scenario in an unguided manner. Afterward, keystroke and in-text features are extracted from the dataset. These are used with an assortment of learning methods to identify emotion hidden in the short text. An accuracy of 86.95% is achieved by fusing text and keystroke features. Whereas, 100% accuracy is obtained for pleasure-displeasure classes of emotions using the fusion of keystroke/text features, tree-based feature selection method, and support vector machine classifier. The present work is also compared with four state-of-the-art techniques for the same task, where the results suggest that the present proposal performs better in terms of accuracy.

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Section: Experiments With Additional Datamentioning

confidence: 99%

Non-Acted Text and Keystrokes Database and Learning Methods to Recognize Emotions

Tahir

Halim

Rahman

et al. 2022

ACM Trans. Multimedia Comput. Commun. Appl.

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show abstract

“…Human emotions are a complex phenomenon, with a variety of basic emotions highly interrelated, showing positive or negative correlations. Positively correlated emotions are more likely to occur together, while negatively correlated emotions rarely occur together [9]. For example, example sentence (a) in Figure 1 labels two basic emotions, sadness and fear, where sadness is the dominant emotion.…”

Section: Introductionmentioning

confidence: 99%

Emotion Wheel Attention-Based Emotion Distribution Learning

Zeng

Chen

et al. 2021

IEEE Access

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Emotion distribution learning is an effective multi-emotion analysis model proposed in recent years. Its core idea is to record the expression degree of examples on each emotion through emotion distribution, which is suitable for handling emotion analysis tasks with emotional ambiguity. To solve the problem that the prior knowledge of emotion psychology is seldom considered in the existing emotion distribution learning methods, we propose an Emotion Wheel Attention based Emotion Distribution Learning (EWA-EDL) model. EWA-EDL generates a prior emotion distribution describing the relevance of emotional psychology for each basic emotion, and then directly integrates the prior knowledge based on the emotion wheel into the deep neural network through the attention mechanism. The deep network of EWA-EDL is trained using an end-to-end approach to learn both emotion distribution prediction and emotion classification tasks. The EWA-EDL architecture includes five main parts: input layer, convolutional layer, pooling layer, attention layer and multi-task loss layer. Extensive comparative experiments on eight commonly used textual emotion datasets show that EWA-EDL outperforms the comparison emotion distribution learning methods on both emotion distribution prediction and emotion classification task.

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“…Emotion extraction via text can be implemented in various levels such as word, sentence, phrase, paragraph or document. These systems are applicable in different fields such as emotion retrieval from suicide notes [9,10], capturing emotions in multimedia tagging [11], detecting insulting sentences in conversations [12], market research [13], e-learning [14] and so on.…”

Section: Introductionmentioning

confidence: 99%

Emotion Extraction from Text Using Fuzzy-Deep Neural Network

Zaremarjal

Yiltas-Kaplan

Lazemi

2021

Lecture Notes in Networks and Systems

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Emotions are one of the most important and affective start points of our decisions in daily life. People express their emotions, consciously or unconsciously, in their behaviors, like speech, facial expressions, textual content, and so on, whether it be in blogs, reviews, or on social media. Due to the rapid growth in textual social media to link people, the emotion extraction from textual data has attracted a lot of attention. This paper performs the sentence-level discrete emotion extraction from text by using Fuzzy Neural Networks (FNN) and Deep Recurrent Neural Networks (DRNN). Much work has been presented to extracting emotion from textual data with precise and constant boundaries, but little of it has considered the inherent uncertainty in natural language. In other words, the high ambiguity of emotion in text data makes an emotionally sentence express multiple emotions at the same time. To deal with this uncertainty we use a Neuro Neural Network (NNN). But from another point of view, by increasing the number of inputs, the number of parameters in NNN growth exponentially. To this end, we extract a low-dimensional semantic representation of input sentence by using a Bi-directional Long Short-Term Memory (Bi-LSTM). Our goal is to achieve better performance with combining the advantages of deep learning in high-level semantic feature extraction to ambiguity handling and the capabilities of fuzzy logic with fuzzy membership degrees to uncertainty handling. Experiments are conducted on the SemEval2007-Task14 dataset that contains 1250 annotated texts with Ekman's basic emotions. The obtained results indicate that our proposed method outperforms the previous methods.

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Exploiting Multi-Emotion Relations at Feature and Label Levels for Emotion Tagging

Cited by 3 publications

References 23 publications

Non-Acted Text and Keystrokes Database and Learning Methods to Recognize Emotions

Non-Acted Text and Keystrokes Database and Learning Methods to Recognize Emotions

Emotion Wheel Attention-Based Emotion Distribution Learning

Emotion Extraction from Text Using Fuzzy-Deep Neural Network

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