Text-Based Intelligent Learning Emotion System

Razek, Mohammed Abdel; Frasson, Claude

doi:10.4236/jilsa.2017.91002

Cited by 13 publications

(10 citation statements)

References 8 publications

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“…For ISEAR dataset, although the biggest number of sentences in train data is anger, the system recognizes joy and fear better than any other emotion categories. Although we cannot explain why our system performs well on joy and fear emotion categories, other researchers also reported that they obtain the best performance in another category instead of anger category [14], [28]. Figure 7 and 8 shows obtained confusion matrix plot from those datasets.…”

Section: Categorical Emotion Recognitionmentioning

confidence: 86%

“…Significant research have been conducted on text emotion recognition, from text corpus construction [5], [6] to feature extraction [7], classifier development [14], [16] and its application [4]. Strapparava and Mihalcea has developed text corpus for text emotion task [5] and presented an explanation of their corpus annotation process in [8].…”

Section: Related Workmentioning

confidence: 99%

“…Razek and Fasson [14] used intelligent learning system to recognize emotion in text. They proposed a text-based emotion recognition approach that uses personal text data to recognize user current emotion.…”

Section: Related Workmentioning

confidence: 99%

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Deep Learning-based Categorical and Dimensional Emotion Recognition for Written and Spoken Text

Atmaja¹

2019

Preprint

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This paper presents a deep learning-based approach for the recognition of categorical and dimensional emotion from both written and spoken texts. The result shows that the system performs better on both categorical and dimensional task (> 60% accuracy and < 20% error) with a larger dataset compared to a smaller dataset. We also found the recognition rate is affected by both the size of the data and the number of emotion categories. On the dimensional task, a larger amount of data consistently provided a better result. Recognition of categorical emotion on a spoken text is easier than on a written text, while on dimensional task, the written text yielded better performance.

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Section: Categorical Emotion Recognitionmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

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Deep Learning-based Categorical and Dimensional Emotion Recognition for Written and Spoken Text

Atmaja¹

2019

Preprint

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“…Razek and Frasson, used Dominant Meaning Classifier (DMC) to recognize emotion from text [46]. A dominant tree is trained on the ISEAR dataset to form seven emotion classes, joy, fear, anger, sadness, disgust, shame, and guilt, which is in line with Carroll [29] discrete emotions.…”

Section: Yasmina Et Al Used Point Wise Mutual Information (Pmi) To Cmentioning

confidence: 99%

Emotions in texts

Pokhun

Chuttur

2020

Bull.Socinf.The.App

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Several studies have used different techniques to detect and identify emotions expressed in various sets of texts corpora. In this paper, we review different emotion models, emotion datasets and the corresponding techniques used for emotion analysis in past studies. We observe that researchers have been using a wide variety of techniques to detect emotions in texts and that there is currently no gold standard on which dataset or which emotion model to use. Consequently, although the field of emotion analysis has gained much momentum in previous years, there seems to be little progress into relevant research with findings that may be useful in real world applications. From our analysis and findings, we urge researchers to consider the development of datasets, evaluation benchmarks and a common platform for sharing achievements in emotion analysis to see further development in the field.

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“…Nowadays, emotion mining based on the social media has become one of the most important tasks in optimizing human-computer interaction. e textual sentiment analysis published by social media such as microblog has also attracted widespread attention, and many related research studies have been conducted [1][2][3][4]. Nevertheless, the emotion information contained in text is limited, and there are several fetters on the identification of technical terms in specific fields.…”

Section: Introductionmentioning

confidence: 99%

Audio‐Textual Emotion Recognition Based on Improved Neural Networks

Cai

Dong

et al. 2019

Mathematical Problems in Engineering

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With the rapid development in social media, single-modal emotion recognition is hard to satisfy the demands of the current emotional recognition system. Aiming to optimize the performance of the emotional recognition system, a multimodal emotion recognition model from speech and text was proposed in this paper. Considering the complementarity between different modes, CNN (convolutional neural network) and LSTM (long short-term memory) were combined in a form of binary channels to learn acoustic emotion features; meanwhile, an effective Bi-LSTM (bidirectional long short-term memory) network was resorted to capture the textual features. Furthermore, we applied a deep neural network to learn and classify the fusion features. The final emotional state was determined by the output of both speech and text emotion analysis. Finally, the multimodal fusion experiments were carried out to validate the proposed model on the IEMOCAP database. In comparison with the single modal, the overall recognition accuracy of text increased 6.70%, and that of speech emotion recognition soared 13.85%. Experimental results show that the recognition accuracy of our multimodal is higher than that of the single modal and outperforms other published multimodal models on the test datasets.

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Text-Based Intelligent Learning Emotion System

Cited by 13 publications

References 8 publications

Deep Learning-based Categorical and Dimensional Emotion Recognition for Written and Spoken Text

Deep Learning-based Categorical and Dimensional Emotion Recognition for Written and Spoken Text

Emotions in texts

Audio‐Textual Emotion Recognition Based on Improved Neural Networks

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