Emotion analysis of microblog based on emotion dictionary and Bi-GRU

Wang, Hao; Zhao, Dequn

doi:10.1109/ipec49694.2020.9115194

Cited by 11 publications

(10 citation statements)

References 5 publications

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“…Among them, the authors of [9,[41][42][43][44] used the research results of previous researchers as the baseline, while the study of [45] was originally proposed in the research of automatic speech recognition. When researchers in [46][47][48][49] applied it to speech emotion recognition, the database used was also inconsistent with the database used in this study. Therefore, this study adopted the model structure proposed in [45][46][47][48][49] and verified the model performance on the three databases used in this study.…”

Section: The Performance Of Hpcb and Its Peer Methodsmentioning

confidence: 95%

“…When researchers in [46][47][48][49] applied it to speech emotion recognition, the database used was also inconsistent with the database used in this study. Therefore, this study adopted the model structure proposed in [45][46][47][48][49] and verified the model performance on the three databases used in this study. Final results are shown in Tables 1-3.…”

Section: The Performance Of Hpcb and Its Peer Methodsmentioning

confidence: 95%

See 1 more Smart Citation

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

2021

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Speech emotion recognition is a substantial component of natural language processing (NLP). It has strict requirements for the effectiveness of feature extraction and that of the acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address the challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recalls on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

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Section: The Performance Of Hpcb and Its Peer Methodsmentioning

confidence: 95%

Section: The Performance Of Hpcb and Its Peer Methodsmentioning

confidence: 95%

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

2021

View full text Add to dashboard Cite

show abstract

“…Among them, literatures [7], [29][30], [32] used the research results of previous researchers as the baseline, while literature [21] was originally proposed in the research of automatic speech recognition. When researchers in literature [34][35][36][37] applied it to speech emotion recognition, the database used was also inconsistent with the database used in this study. Therefore, this study adopts the model structure proposed in the literature [21], [34][35][36][37], and verifies the model performance on the four databases used in this study.…”

Section: The Performance Of Hpcb and Its Peer Methodsmentioning

confidence: 98%

“…When researchers in literature [34][35][36][37] applied it to speech emotion recognition, the database used was also inconsistent with the database used in this study. Therefore, this study adopts the model structure proposed in the literature [21], [34][35][36][37], and verifies the model performance on the four databases used in this study. Final results are shown in Table 1-Table 3.…”

Section: The Performance Of Hpcb and Its Peer Methodsmentioning

confidence: 98%

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

Zhang¹,

Huang²,

Han³

2021

Preprint

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Speech emotion recognition remains a heavy lifting in natural language processing. It has strict requirements to the effectiveness of feature extraction and that of acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address these challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recall on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.

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“…Experiments show that the model-based Chinese text sentiment analysis can achieve an accuracy of 87.1 percent. [16]. To measure emotion scores in the entire data collection, the enhanced dictionary classification system is used, and data with high or low scores are explicitly marked; the rest of the method is based on emotion dictionary and Bi-GRU fusion model to calculate emotion score.…”

Section: -Literature Surveymentioning

confidence: 99%

Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification

Chandra¹,

Ahuja²,

Khatri³

et al. 2021

JIST

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The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time and effort and may impact their product sales, so to make it quick these organizations have asked their IT to leverage machine learning algorithms to process such text on a real-time basis. Gated recurrent units (GRUs) algorithms which is an extension of the Recurrent Neural Network and referred to as gating mechanism in the network helps provides such mechanism. Recurrent Neural Networks (RNN) has demonstrated to be the main alternative to deal with sequence classification and have demonstrated satisfactory to keep up the information from past outcomes and influence those outcomes for performance adjustment. The GRU model helps in rectifying gradient problems which can help benefit multiple use cases by making this model learn long-term dependencies in text data structures. A few of the use cases that follow aresentiment analysis for NLP. GRU with RNN is being used as it would need to retain long-term dependencies. This paper presents a text classification technique using a sequential word embedding processed using gated recurrent unit sigmoid function in a Recurrent neural network. This paper focuses on classifying text using the Gated Recurrent Units method that makes use of the framework for embedding fixed size, matrix text. It helps specifically inform the network of long-term dependencies. We leveraged the GRU model on the movie review dataset with a classification accuracy of 87%.

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Emotion analysis of microblog based on emotion dictionary and Bi-GRU

Cited by 11 publications

References 5 publications

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition

Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification

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