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Implicit mood computing via LSTM and semantic mapping

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Abstract

This article proposes an implicit mood computing system. The implicit mood computing task is a part of affective computing. Previous works in affective computing mostly focus on twitters, blogs, movie interviews, and news corpus. These works detect sentiment polarity (positive/negative), emotion types (joy, sadness, anger, etc.), or mood types (boring, tired, happy, etc.) of the text. Different from previous studies, our work focuses on the literature texts and detects the implicit mood of them. The implicit mood is sometimes discussed as the tone or the atmosphere of the text. The implicit mood is an important affective feature in the literature such as poetry, prose, and drama. Our work regards the implicit mood as a semantic phenomenon. We capture the feature of implicit mood via a semantic mapping approach and the long short-term memory neural network. The proposed system is capable of identifying 12 kinds of implicit moods with a promising result.

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Notes

  1. https://yuwen.chazidian.com/.

  2. https://yuwen.chazidian.com/.

  3. http://vdisk.weibo.com/s/qGrIviGdExvx.

  4. http://hanyu.baidu.com/.

  5. http://www.sanwen8.cn/.

  6. https://www.sanwen.net/.

  7. http://www.juzimi.com/.

  8. http://www.zyzw.com/.

  9. http://www.wenku1.com/.

  10. We used the Chinese version. http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Project 61075058).

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  1. School of Informatics, Xiamen University, Xiamen, China

    Chang Su, Junchao Li, Ying Peng & Yijiang Chen

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  1. Chang Su
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  2. Junchao Li
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  3. Ying Peng
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  4. Yijiang Chen
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Correspondence to Chang Su.

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Parts of the prediction results of the proposed method

Parts of the prediction results of the proposed method

Samples are shown in both Chinese and English translations for reading convenience.

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Su, C., Li, J., Peng, Y. et al. Implicit mood computing via LSTM and semantic mapping. Soft Comput 24, 15795–15809 (2020). https://doi.org/10.1007/s00500-020-04909-5

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