A Context Integrated Model for Multi-label Emotion Detection

Samy, Ahmed; El-Beltagy, Samhaa R.; Hassanien, Ehab

doi:10.1016/j.procs.2018.10.461

Cited by 33 publications

(21 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

Section: ) Comparison With State-of-the-artmentioning

confidence: 92%

“…On the SemEval-2018 dataset, the HEF+DF model outperformed the Samy et al [7] model, achieving a 0.7% improvement in F macro . However, Samy et al [7] outperformed the HEF+DF model, achieving 2% and 1.7% differences in Jaccard accuracy and F micro , respectively. Moreover, the HEF+DF model outperformed Badaro et al [2] by 2.3%, 1.3%, and 4.1% on Jaccard accuracy, F micro , and F macro , respectively.…”

Section: ) Comparison With State-of-the-artmentioning

confidence: 92%

“…Last, it outperformed Abdullah and Shaikh [4] by 5.9%, 5.5%, and 4.4% on Jaccard accuracy, F micro , and F macro , respectively. However, Samy et al [7] outperformed the DF model by 2.7%, 2.1%, and 0.5% on Jaccard accuracy, F micro , and F macro , respectively. The HEF model did not perform as well as the DF and HEF+DF models.…”

Section: ) Comparison With State-of-the-artmentioning

confidence: 93%

“…Samy et al [7] proposed a context-aware gated recurrent unit (C-GRU). The preprocessing steps included removing links, hashtag symbols, user mentions, diacritics, and elongations.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Hybrid Feature Model for Emotion Recognition in Arabic Text

Alswaidan

Menaï

2020

IEEE Access

View full text Add to dashboard Cite

In recent years, research into developing state-of-the-art models for Arabic natural language processing tasks has gained momentum. These models must address the added difficulties related to the nature and structure of the Arabic language. In this paper, we propose three models, a human-engineered feature-based (HEF) model, a deep feature-based (DF) model, and a hybrid of both models (HEF+DF) for emotion recognition in Arabic text. We evaluated the performance of the proposed models on the SemEval-2018, IAEDS, and AETD datasets by comparing the performances of those models on each emotion label. We also compared the model performances with those of other state-of-the-art models. The results show that the HEF+DF model outperformed the DF and HEF models on all datasets. The DF model performed better than the HEF model on the SemEval-2018 and AETD datasets, while the HEF model performed better than the DF model on the IAEDS dataset. The HEF+DF model outperformed the state-of-the-art models in terms of accuracy, weighted-average precision, weighted-average recall, and weighted-average F-score on the AETD dataset and in terms of accuracy, macro-averaged precision, macro-averaged recall, and macroaveraged F-score on the IAEDS dataset. It also achieved the best macro-averaged F-score and the second-best Jaccard accuracy and micro-averaged F-score on the SemEval-2018 dataset. INDEX TERMS Arabic natural language processing, deep learning, emotion recognition, small dataset.

show abstract

Section: ) Comparison With State-of-the-artmentioning

confidence: 92%

Section: ) Comparison With State-of-the-artmentioning

confidence: 92%

Section: ) Comparison With State-of-the-artmentioning

confidence: 93%

“…Samy et al [7] proposed a context-aware gated recurrent unit (C-GRU). The preprocessing steps included removing links, hashtag symbols, user mentions, diacritics, and elongations.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Feature Model for Emotion Recognition in Arabic Text

Alswaidan

Menaï

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In an attempt to detect emotion from Twitter data, [52] made use of C-GRU (Context-aware Gated Recurrent Units) for context extraction when determining user feelings. Emotions were classified using the twelve discrete emotions: Anger, Anticipation, Disgust, Fear, Joy, Love, Optimism, Pessimism, Sadness, Surprise, Trust, and Neutral.…”

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

confidence: 99%

Emotions in texts

Pokhun

Chuttur

2020

Bull.Socinf.The.App

View full text Add to dashboard Cite

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.

show abstract

An emotion analysis scheme based on Gray Wolf optimization and deep learning

Uçan

Dörterler

Sezer

2021

Concurrency and Computation

View full text Add to dashboard Cite

Automatic detection of emotions in textual data masses provides priceless opportunities for researchers and also it is inevitable for practitioners. The unfavorable factors involved in text data cause ambiguity and adversely affect the performances of emotion classifiers. Although deep learning approaches spark off significantly successive results, the obtained performances of the classifiers in the literature are commonly evaluated as the overall accuracy. This incomplete evaluation ignores inner class performance and overall accuracy can behave as a hopeful evaluator. In this study, we employed deep learning and meta‐heuristic optimization methods together in order to resolve the ambiguity issue. Moreover, the decision mechanism of a conventional deep learning model is equipped with optimal emotion vectors obtained by optimization processes for each emotion class. Experimental results show that the proposed approach improves the inner class performance by maintaining the overall accuracy scores.

show abstract

A Context Integrated Model for Multi-label Emotion Detection

Cited by 33 publications

References 38 publications

Hybrid Feature Model for Emotion Recognition in Arabic Text

Hybrid Feature Model for Emotion Recognition in Arabic Text

Emotions in texts

An emotion analysis scheme based on Gray Wolf optimization and deep learning

Contact Info

Product

Resources

About