References
[1]. Q. Dong and R. Fang, ‘A Deep Learning-Based Text Emotional Analysis Framework for Yellow River Basin Tourism Culture’, Mobile Information Systems, vol. 2022, pp. 1–9, Sep. 2022, doi: 10.1155/2022/6836223.
[2]. F. Li, H. Tang, Y. Zou, Y. Huang, Y. Feng, and L. Peng, ‘Research on information security in text emotional steganography based on machine learning’, Enterprise Information Systems, vol. 15, no. 7, pp. 984–1001, Aug. 2021, doi: 10.1080/17517575.2020.1720827.
[3]. R. Feldman, ‘Techniques and applications for sentiment analysis’, Commun. ACM, vol. 56, no. 4, pp. 82–89, Apr. 2013, doi: 10.1145/2436256.2436274.
[4]. W. Medhat, A. Hassan, and H. Korashy, ‘Sentiment analysis algorithms and applications: A survey’, Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, Dec. 2014, doi: 10.1016/j.asej.2014.04.011.
[5]. E. M. Forster, Maurice, Repr. London: Penguin, 1993.
[6]. G. K. Verma and U. S. Tiwary, ‘Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals’, NeuroImage, vol. 102, pp. 162–172, Nov. 2014, doi: 10.1016/j.neuroimage.2013.11.007.
[7]. J. Hofmann, E. Troiano, K. Sassenberg, and R. Klinger, ‘Appraisal Theories for Emotion Classification in Text’. arXiv, Nov. 03, 2020. Accessed: Feb. 19, 2023. [Online]. Available: http://arxiv.org/abs/2003.14155
[8]. J. L. Tracy and D. Randles, ‘Four Models of Basic Emotions: A Review of Ekman and Cordaro, Izard, Levenson, and Panksepp and Watt’, Emotion Review, vol. 3, no. 4, pp. 397–405, Oct. 2011, doi: 10.1177/1754073911410747.
[9]. E. Tromp and M. Pechenizkiy, ‘Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik’s Wheel’. arXiv, Dec. 15, 2014. Accessed: Feb. 18, 2023. [Online]. Available: http://arxiv.org/abs/1412.4682
[10]. J. Staiano and M. Guerini, ‘DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News’. arXiv, May 07, 2014. Accessed: Feb. 20, 2023. [Online]. Available: http://arxiv.org/abs/1405.1605
[11]. L. De Bruyne, P. Atanasova, and I. Augenstein, ‘Joint emotion label space modeling for affect lexica’, Computer Speech & Language, vol. 71, p. 101257, Jan. 2022, doi: 10.1016/j.csl.2021.101257.
[12]. S. M. Mohammad and P. D. Turney, ‘CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON’, Computational Intelligence, vol. 29, no. 3, pp. 436–465, Aug. 2013, doi: 10.1111/j.1467-8640.2012.00460.x.
[13]. H. Li and F. Ren, ‘The study on text emotional orientation based on a three-dimensional emotion space model’, in 2009 International Conference on Natural Language Processing and Knowledge Engineering, Dalian, China, Sep. 2009, pp. 1–6. doi: 10.1109/NLPKE.2009.5313815.
[14]. O. Araque, L. Gatti, J. Staiano, and M. Guerini, ‘DepecheMood++: A Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques’, IEEE Trans. Affective Comput., vol. 13, no. 1, pp. 496–507, Jan. 2022, doi: 10.1109/TAFFC.2019.2934444.
[15]. A. Bandhakavi, N. Wiratunga, D. P, and S. Massie, ‘Generating a Word-Emotion Lexicon from #Emotional Tweets’, in Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014), Dublin, Ireland, 2014, pp. 12–21. doi: 10.3115/v1/S14-1002.
[16]. D. Xu, Z. Tian, R. Lai, X. Kong, Z. Tan, and W. Shi, ‘Deep learning based emotion analysis of microblog texts’, Information Fusion, vol. 64, pp. 1–11, Dec. 2020, doi: 10.1016/j.inffus.2020.06.002.
[17]. Y. Zhang, J. Fu, D. She, Y. Zhang, S. Wang, and J. Yang, ‘Text Emotion Distribution Learning via Multi-Task Convolutional Neural Network’, in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, Jul. 2018, pp. 4595–4601. doi: 10.24963/ijcai.2018/639.
[18]. S. Wang, M. Huang, and Z. Deng, ‘Densely Connected CNN with Multi-scale Feature Attention for Text Classification’, in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, Jul. 2018, pp. 4468–4474. doi: 10.24963/ijcai.2018/621.
[19]. S. M. Mohammad and S. Kiritchenko, ‘Using Hashtags to Capture Fine Emotion Categories from Tweets: USING HASHTAGS TO CAPTURE FINE EMOTION CATEGORIES’, Computational Intelligence, vol. 31, no. 2, pp. 301–326, May 2015, doi: 10.1111/coin.12024.
[20]. B. Ghanem, P. Rosso, and F. Rangel, ‘An Emotional Analysis of False Information in Social Media and News Articles’, ACM Trans. Internet Technol., vol. 20, no. 2, pp. 1–18, May 2020, doi: 10.1145/3381750.
[21]. E. Kim and R. Klinger, ‘A Survey on Sentiment and Emotion Analysis for Computational Literary Studies’. Jul. 11, 2022. doi: 10.17175/2019_008.
[22]. S. M. Mohammad and P. D. Turney, ‘Nrc emotion lexicon’, 2013.
[23]. X. Zhu, S. Kiritchenko, and S. Mohammad, ‘NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets’, in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, 2014, pp. 443–447. doi: 10.3115/v1/S14-2077.
Cite this article
Yimu,X. (2023). Ethnic minorities' mentality and homosexuality psychology in literature: A text emotion analysis with NRC lexicon. Applied and Computational Engineering,13,240-250.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Q. Dong and R. Fang, ‘A Deep Learning-Based Text Emotional Analysis Framework for Yellow River Basin Tourism Culture’, Mobile Information Systems, vol. 2022, pp. 1–9, Sep. 2022, doi: 10.1155/2022/6836223.
[2]. F. Li, H. Tang, Y. Zou, Y. Huang, Y. Feng, and L. Peng, ‘Research on information security in text emotional steganography based on machine learning’, Enterprise Information Systems, vol. 15, no. 7, pp. 984–1001, Aug. 2021, doi: 10.1080/17517575.2020.1720827.
[3]. R. Feldman, ‘Techniques and applications for sentiment analysis’, Commun. ACM, vol. 56, no. 4, pp. 82–89, Apr. 2013, doi: 10.1145/2436256.2436274.
[4]. W. Medhat, A. Hassan, and H. Korashy, ‘Sentiment analysis algorithms and applications: A survey’, Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, Dec. 2014, doi: 10.1016/j.asej.2014.04.011.
[5]. E. M. Forster, Maurice, Repr. London: Penguin, 1993.
[6]. G. K. Verma and U. S. Tiwary, ‘Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals’, NeuroImage, vol. 102, pp. 162–172, Nov. 2014, doi: 10.1016/j.neuroimage.2013.11.007.
[7]. J. Hofmann, E. Troiano, K. Sassenberg, and R. Klinger, ‘Appraisal Theories for Emotion Classification in Text’. arXiv, Nov. 03, 2020. Accessed: Feb. 19, 2023. [Online]. Available: http://arxiv.org/abs/2003.14155
[8]. J. L. Tracy and D. Randles, ‘Four Models of Basic Emotions: A Review of Ekman and Cordaro, Izard, Levenson, and Panksepp and Watt’, Emotion Review, vol. 3, no. 4, pp. 397–405, Oct. 2011, doi: 10.1177/1754073911410747.
[9]. E. Tromp and M. Pechenizkiy, ‘Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik’s Wheel’. arXiv, Dec. 15, 2014. Accessed: Feb. 18, 2023. [Online]. Available: http://arxiv.org/abs/1412.4682
[10]. J. Staiano and M. Guerini, ‘DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News’. arXiv, May 07, 2014. Accessed: Feb. 20, 2023. [Online]. Available: http://arxiv.org/abs/1405.1605
[11]. L. De Bruyne, P. Atanasova, and I. Augenstein, ‘Joint emotion label space modeling for affect lexica’, Computer Speech & Language, vol. 71, p. 101257, Jan. 2022, doi: 10.1016/j.csl.2021.101257.
[12]. S. M. Mohammad and P. D. Turney, ‘CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON’, Computational Intelligence, vol. 29, no. 3, pp. 436–465, Aug. 2013, doi: 10.1111/j.1467-8640.2012.00460.x.
[13]. H. Li and F. Ren, ‘The study on text emotional orientation based on a three-dimensional emotion space model’, in 2009 International Conference on Natural Language Processing and Knowledge Engineering, Dalian, China, Sep. 2009, pp. 1–6. doi: 10.1109/NLPKE.2009.5313815.
[14]. O. Araque, L. Gatti, J. Staiano, and M. Guerini, ‘DepecheMood++: A Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques’, IEEE Trans. Affective Comput., vol. 13, no. 1, pp. 496–507, Jan. 2022, doi: 10.1109/TAFFC.2019.2934444.
[15]. A. Bandhakavi, N. Wiratunga, D. P, and S. Massie, ‘Generating a Word-Emotion Lexicon from #Emotional Tweets’, in Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014), Dublin, Ireland, 2014, pp. 12–21. doi: 10.3115/v1/S14-1002.
[16]. D. Xu, Z. Tian, R. Lai, X. Kong, Z. Tan, and W. Shi, ‘Deep learning based emotion analysis of microblog texts’, Information Fusion, vol. 64, pp. 1–11, Dec. 2020, doi: 10.1016/j.inffus.2020.06.002.
[17]. Y. Zhang, J. Fu, D. She, Y. Zhang, S. Wang, and J. Yang, ‘Text Emotion Distribution Learning via Multi-Task Convolutional Neural Network’, in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, Jul. 2018, pp. 4595–4601. doi: 10.24963/ijcai.2018/639.
[18]. S. Wang, M. Huang, and Z. Deng, ‘Densely Connected CNN with Multi-scale Feature Attention for Text Classification’, in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, Jul. 2018, pp. 4468–4474. doi: 10.24963/ijcai.2018/621.
[19]. S. M. Mohammad and S. Kiritchenko, ‘Using Hashtags to Capture Fine Emotion Categories from Tweets: USING HASHTAGS TO CAPTURE FINE EMOTION CATEGORIES’, Computational Intelligence, vol. 31, no. 2, pp. 301–326, May 2015, doi: 10.1111/coin.12024.
[20]. B. Ghanem, P. Rosso, and F. Rangel, ‘An Emotional Analysis of False Information in Social Media and News Articles’, ACM Trans. Internet Technol., vol. 20, no. 2, pp. 1–18, May 2020, doi: 10.1145/3381750.
[21]. E. Kim and R. Klinger, ‘A Survey on Sentiment and Emotion Analysis for Computational Literary Studies’. Jul. 11, 2022. doi: 10.17175/2019_008.
[22]. S. M. Mohammad and P. D. Turney, ‘Nrc emotion lexicon’, 2013.
[23]. X. Zhu, S. Kiritchenko, and S. Mohammad, ‘NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets’, in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, 2014, pp. 443–447. doi: 10.3115/v1/S14-2077.