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

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

研究成果: Conference contribution

5 引文 (Scopus)

摘要

This study proposes a long-short term memory (LSTM)-based approach to text emotion recognition based on semantic word vector and emotional word vector of the input text. For each word in an input text, the semantic word vector is extracted from the word 2vec model. Besides, each lexical word is projected to all the emotional words defined in an affective lexicon to derive an emotional word vector. An autoencoder is then adopted to obtain the bottleneck features from the emotional word vector for dimensionality reduction. The autoencoder bottleneck features are then concatenated with the features in the semantic word vector to form the final textual features for emotion recognition. Finally, given the textual feature sequence of the entire sentence, the LSTM is used for emotion recognition by modeling the contextual emotion evolution of the input text. For evaluation, the NLPCC-MHMC-TE database containing seven emotion categories: anger, boredom, disgust, anxiety, happiness, sadness, and surprise was constructed and used. Five-fold cross-validation was employed to evaluate the performance of the proposed method. Experimental results show that the proposed LSTM-based method achieved a recognition accuracy of 70.66%, improving 5.33% compared with the CNN-based method. Besides, the proposed method based on integration of the semantic word vector and emotional word vector of the input text outperformed that using the individual feature vector.

原文English
主出版物標題2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781538653111
DOIs
出版狀態Published - 2018 九月 21
事件1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018 - Beijing, China
持續時間: 2018 五月 202018 五月 22

出版系列

名字2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018

Other

Other1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018
國家China
城市Beijing
期間18-05-2018-05-22

指紋

Semantics
Long short-term memory

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Signal Processing
  • Artificial Intelligence

引用此文

Su, M. H., Wu, C. H., Huang, K. Y., & Hong, Q. B. (2018). LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors. 於 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018 [8470378] (2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACIIAsia.2018.8470378
Su, Ming Hsiang ; Wu, Chung Hsien ; Huang, Kun Yi ; Hong, Qian Bei. / LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors. 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018).
@inproceedings{6535648005b3464bb1c0de3f6d23b9f7,
title = "LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors",
abstract = "This study proposes a long-short term memory (LSTM)-based approach to text emotion recognition based on semantic word vector and emotional word vector of the input text. For each word in an input text, the semantic word vector is extracted from the word 2vec model. Besides, each lexical word is projected to all the emotional words defined in an affective lexicon to derive an emotional word vector. An autoencoder is then adopted to obtain the bottleneck features from the emotional word vector for dimensionality reduction. The autoencoder bottleneck features are then concatenated with the features in the semantic word vector to form the final textual features for emotion recognition. Finally, given the textual feature sequence of the entire sentence, the LSTM is used for emotion recognition by modeling the contextual emotion evolution of the input text. For evaluation, the NLPCC-MHMC-TE database containing seven emotion categories: anger, boredom, disgust, anxiety, happiness, sadness, and surprise was constructed and used. Five-fold cross-validation was employed to evaluate the performance of the proposed method. Experimental results show that the proposed LSTM-based method achieved a recognition accuracy of 70.66{\%}, improving 5.33{\%} compared with the CNN-based method. Besides, the proposed method based on integration of the semantic word vector and emotional word vector of the input text outperformed that using the individual feature vector.",
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Su, MH, Wu, CH, Huang, KY & Hong, QB 2018, LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors. 於 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018., 8470378, 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018, Institute of Electrical and Electronics Engineers Inc., 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018, Beijing, China, 18-05-20. https://doi.org/10.1109/ACIIAsia.2018.8470378

LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors. / Su, Ming Hsiang; Wu, Chung Hsien; Huang, Kun Yi; Hong, Qian Bei.

2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8470378 (2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018).

研究成果: Conference contribution

TY - GEN

T1 - LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors

AU - Su, Ming Hsiang

AU - Wu, Chung Hsien

AU - Huang, Kun Yi

AU - Hong, Qian Bei

PY - 2018/9/21

Y1 - 2018/9/21

N2 - This study proposes a long-short term memory (LSTM)-based approach to text emotion recognition based on semantic word vector and emotional word vector of the input text. For each word in an input text, the semantic word vector is extracted from the word 2vec model. Besides, each lexical word is projected to all the emotional words defined in an affective lexicon to derive an emotional word vector. An autoencoder is then adopted to obtain the bottleneck features from the emotional word vector for dimensionality reduction. The autoencoder bottleneck features are then concatenated with the features in the semantic word vector to form the final textual features for emotion recognition. Finally, given the textual feature sequence of the entire sentence, the LSTM is used for emotion recognition by modeling the contextual emotion evolution of the input text. For evaluation, the NLPCC-MHMC-TE database containing seven emotion categories: anger, boredom, disgust, anxiety, happiness, sadness, and surprise was constructed and used. Five-fold cross-validation was employed to evaluate the performance of the proposed method. Experimental results show that the proposed LSTM-based method achieved a recognition accuracy of 70.66%, improving 5.33% compared with the CNN-based method. Besides, the proposed method based on integration of the semantic word vector and emotional word vector of the input text outperformed that using the individual feature vector.

AB - This study proposes a long-short term memory (LSTM)-based approach to text emotion recognition based on semantic word vector and emotional word vector of the input text. For each word in an input text, the semantic word vector is extracted from the word 2vec model. Besides, each lexical word is projected to all the emotional words defined in an affective lexicon to derive an emotional word vector. An autoencoder is then adopted to obtain the bottleneck features from the emotional word vector for dimensionality reduction. The autoencoder bottleneck features are then concatenated with the features in the semantic word vector to form the final textual features for emotion recognition. Finally, given the textual feature sequence of the entire sentence, the LSTM is used for emotion recognition by modeling the contextual emotion evolution of the input text. For evaluation, the NLPCC-MHMC-TE database containing seven emotion categories: anger, boredom, disgust, anxiety, happiness, sadness, and surprise was constructed and used. Five-fold cross-validation was employed to evaluate the performance of the proposed method. Experimental results show that the proposed LSTM-based method achieved a recognition accuracy of 70.66%, improving 5.33% compared with the CNN-based method. Besides, the proposed method based on integration of the semantic word vector and emotional word vector of the input text outperformed that using the individual feature vector.

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M3 - Conference contribution

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T3 - 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018

BT - 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Su MH, Wu CH, Huang KY, Hong QB. LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors. 於 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8470378. (2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018). https://doi.org/10.1109/ACIIAsia.2018.8470378