Semantic-Emotion Neural Network for Emotion Recognition From Text
Emotion detection and recognition from text is a recent essential research area in Natural Language Processing (NLP) which may reveal some valuable input to a variety of purposes. Nowadays, writings take many forms of social media posts, micro-blogs, news articles, customer review, etc., and the con...
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doaj-ce69c8db7ddc427cb45224881c3b03a12021-04-05T17:24:48ZengIEEEIEEE Access2169-35362019-01-01711186611187810.1109/ACCESS.2019.29345298794541Semantic-Emotion Neural Network for Emotion Recognition From TextErdenebileg Batbaatar0https://orcid.org/0000-0002-9724-8955Meijing Li1Keun Ho Ryu2https://orcid.org/0000-0003-0394-9054School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South KoreaCollege of Information Engineering, Shanghai Maritime University, Shanghai, ChinaFaculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, VietnamEmotion detection and recognition from text is a recent essential research area in Natural Language Processing (NLP) which may reveal some valuable input to a variety of purposes. Nowadays, writings take many forms of social media posts, micro-blogs, news articles, customer review, etc., and the content of these short-texts can be a useful resource for text mining to discover an unhide various aspects, including emotions. The previously presented models mainly adopted word embedding vectors that represent rich semantic/syntactic information and those models cannot capture the emotional relationship between words. Recently, some emotional word embeddings are proposed but it requires semantic and syntactic information vice versa. To address this issue, we proposed a novel neural network architecture, called SENN (Semantic-Emotion Neural Network) which can utilize both semantic/syntactic and emotional information by adopting pre-trained word representations. SENN model has mainly two sub-networks, the first sub-network uses bidirectional Long-Short Term Memory (BiLSTM) to capture contextual information and focuses on semantic relationship, the second sub-network uses the convolutional neural network (CNN) to extract emotional features and focuses on the emotional relationship between words from the text. We conducted a comprehensive performance evaluation for the proposed model using standard real-world datasets. We adopted the notion of Ekman's six basic emotions. The experimental results show that the proposed model achieves a significantly superior quality of emotion recognition with various state-of-the-art approaches and further can be improved by other emotional word embeddings.https://ieeexplore.ieee.org/document/8794541/Emotion recognitionnatural language processingdeep learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Erdenebileg Batbaatar Meijing Li Keun Ho Ryu |
spellingShingle |
Erdenebileg Batbaatar Meijing Li Keun Ho Ryu Semantic-Emotion Neural Network for Emotion Recognition From Text IEEE Access Emotion recognition natural language processing deep learning |
author_facet |
Erdenebileg Batbaatar Meijing Li Keun Ho Ryu |
author_sort |
Erdenebileg Batbaatar |
title |
Semantic-Emotion Neural Network for Emotion Recognition From Text |
title_short |
Semantic-Emotion Neural Network for Emotion Recognition From Text |
title_full |
Semantic-Emotion Neural Network for Emotion Recognition From Text |
title_fullStr |
Semantic-Emotion Neural Network for Emotion Recognition From Text |
title_full_unstemmed |
Semantic-Emotion Neural Network for Emotion Recognition From Text |
title_sort |
semantic-emotion neural network for emotion recognition from text |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Emotion detection and recognition from text is a recent essential research area in Natural Language Processing (NLP) which may reveal some valuable input to a variety of purposes. Nowadays, writings take many forms of social media posts, micro-blogs, news articles, customer review, etc., and the content of these short-texts can be a useful resource for text mining to discover an unhide various aspects, including emotions. The previously presented models mainly adopted word embedding vectors that represent rich semantic/syntactic information and those models cannot capture the emotional relationship between words. Recently, some emotional word embeddings are proposed but it requires semantic and syntactic information vice versa. To address this issue, we proposed a novel neural network architecture, called SENN (Semantic-Emotion Neural Network) which can utilize both semantic/syntactic and emotional information by adopting pre-trained word representations. SENN model has mainly two sub-networks, the first sub-network uses bidirectional Long-Short Term Memory (BiLSTM) to capture contextual information and focuses on semantic relationship, the second sub-network uses the convolutional neural network (CNN) to extract emotional features and focuses on the emotional relationship between words from the text. We conducted a comprehensive performance evaluation for the proposed model using standard real-world datasets. We adopted the notion of Ekman's six basic emotions. The experimental results show that the proposed model achieves a significantly superior quality of emotion recognition with various state-of-the-art approaches and further can be improved by other emotional word embeddings. |
topic |
Emotion recognition natural language processing deep learning |
url |
https://ieeexplore.ieee.org/document/8794541/ |
work_keys_str_mv |
AT erdenebilegbatbaatar semanticemotionneuralnetworkforemotionrecognitionfromtext AT meijingli semanticemotionneuralnetworkforemotionrecognitionfromtext AT keunhoryu semanticemotionneuralnetworkforemotionrecognitionfromtext |
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