LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors

Su, Ming-Hsiang; Wu, Chung-Hsien; Huang, Kun-Yi; Hong, Qian-Bei

doi:10.1109/aciiasia.2018.8470378

Cited by 39 publications

(18 citation statements)

References 6 publications

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“…Well‐known pretrained word embeddings such as Word2Vec, 44 GloVe, 45 and FastText 46 are used as input term weights 42,47 . Unlike RNN, one‐ or two‐dimensional convolution processes are performed by using different filter sizes in CNN architecture 7,41,42,47‐49 . Furthermore, in a recent pretrained language model‐based approach called as BERT, 50 is used to figure out the input representation on emotion analysis task in the study of Reference 4 .…”

Section: Literature Summarymentioning

confidence: 99%

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

Uçan

Dörterler

Sezer

2021

Concurrency and Computation

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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.

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Section: Literature Summarymentioning

confidence: 99%

“…The researchers combined the predictions produced by weak classifiers in their study 36 . In all of those DL‐based studies, the highest observed classification performance is ranging between 53% 3 and 70% 42 …”

Section: Literature Summarymentioning

confidence: 99%

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

Uçan

Dörterler

Sezer

2021

Concurrency and Computation

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“…It is the implicit state of the previous moment 1 t h − and the input at the current moment t X . The output of the memory gate t i and temporary cell state ~t C is calculated by the following formula [20] [24].…”

Section: Commentsmentioning

confidence: 99%

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

et al. 2020

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With the rapid development of social media, danmaku video provides a platform for users to communicate online. To some extent, danmaku video provides emotional timing information and an innovative method to analyze video data. In the age of big data, studying the characteristics of danmaku and its emotional tendencies can not only help us understand the psychological characteristics of users but also feedback the effective information of users to video platforms, which can help the platforms optimize related short video recommendations so that it can provide a more accurate solution for the selection of audiences during video production. However, danmaku is different from traditional comments. Current emotion classification methods are only suitable for two-dimensional classification which are not suitable for danmaku emotion analysis. Aiming at the problems such as the colloquialism, diversity, spelling errors, structural non-linearity informal language on the Internet, diversity of social topics, and context dependency of emotion analysis of the danmaku data, this paper proposes an improved emotion analysis model based on Bi-LSTM model to classify the further four-dimensional emotions of Pleasure, Anger, Sorrow and Joy. Furthermore, we add tags such as comment time and user name to the danmaku information. Experimental results show that the improved model has higher Accuracy, Recall, Precision, and F1-Score under the same conditions compared with the CNN and SVM. The classification effect of improved model is close to the SOTA. Experimental results also show that the improved model can be effectively applied to the analysis of irregular danmaku emotion.

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“…59-69 Pokhun & Chuttur (Analyzing emotions in texts) 58.6% for general tweets. Another method consisting of the use of LSTM on text emotion recognition was further proposed by Su et al (2018). The method was tested against Natural Language Processing and Chinese Computing (NLPCC) database which contains seven emotion categories: anger, boredom, disgust, anxiety, happiness, sadness, surprise and results obtained indicated an accuracy of 70.66%.…”

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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LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors

Cited by 39 publications

References 6 publications

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

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

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

Emotions in texts

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