Improving Mandarin Emotional Vocabulary in Application Writing Using Deep Learning Models

[1]Chen, W. (2022, February). Analysis on Negative Transfer of Chinese to Vocational English Practical Writing. In 2021 International Conference on Education, Language and Art (ICELA 2021) (pp. 920-924). Atlantis Press. ChenW. (2022, February). Analysis on Negative Transfer of Chinese to Vocational English Practical Writing. In 2021 International Conference on Education, Language and Art (ICELA 2021) (pp. 920-924). Atlantis Press.Search in Google Scholar

[2]Tao, W., & Yan, J. (2019, October). A Study Of the Flipped Classroom Model In Teaching Practical Writing. In 2nd International Conference on Contemporary Education, Social Sciences and Ecological Studies (CESSES 2019) (pp. 366-368). Atlantis Press. TaoW. & YanJ. (2019, October). A Study Of the Flipped Classroom Model In Teaching Practical Writing. In 2nd International Conference on Contemporary Education, Social Sciences and Ecological Studies (CESSES 2019) (pp. 366-368). Atlantis Press.Search in Google Scholar

[3]Grčić, L., & Simeunović, V. (2024). Enhancing Emotional Vocabulary For Psychological Literacy. Hum: časopis Filozofskog fakulteta Sveučilišta u Mostaru, 19(31), 63-82. GrčićL. & SimeunovićV. (2024). Enhancing Emotional Vocabulary For Psychological Literacy. Hum: časopis Filozofskog fakulteta Sveučilišta u Mostaru, 19(31), 63-82.Search in Google Scholar

[4]Hsieh, M. Y., Lin, T. J., Sallade, R., Ha, S. Y., Kraatz, E., & Shin, S. (2021). A collaborative small-group discussion approach to improving fifth graders’ use of academic, relational, social, and emotional vocabulary. International Journal of Educational Research, 106, 101744. HsiehM. Y., LinT. J., SalladeR., HaS. Y., KraatzE. & ShinS. (2021). A collaborative small-group discussion approach to improving fifth graders’ use of academic, relational, social, and emotional vocabulary. International Journal of Educational Research, 106, 101744.Search in Google Scholar

[5]Khan, M., Jan, B., Farman, H., Ahmad, J., Farman, H., & Jan, Z. (2019). Deep learning methods and applications. Deep learning: convergence to big data analytics, 31-42. KhanM., JanB., FarmanH., AhmadJ., FarmanH. & JanZ. (2019). Deep learning methods and applications. Deep learning: convergence to big data analytics, 31-42.Search in Google Scholar

[6]Ullah, A., Khan, S. N., & Nawi, N. M. (2023). Review on sentiment analysis for text classification techniques from 2010 to 2021. Multimedia Tools and Applications, 82(6), 8137-8193. UllahA., KhanS. N. & NawiN. M. (2023). Review on sentiment analysis for text classification techniques from 2010 to 2021. Multimedia Tools and Applications, 82(6), 8137-8193.Search in Google Scholar

[7]Wu, H., Liu, Y., & Wang, J. (2020). Review of Text Classification Methods on Deep Learning. Computers, Materials & Continua, 63(3). WuH., LiuY. & WangJ. (2020). Review of Text Classification Methods on Deep Learning. Computers, Materials & Continua, 63(3).Search in Google Scholar

[8]Xu, G., Yu, Z., Yao, H., Li, F., Meng, Y., & Wu, X. (2019). Chinese text sentiment analysis based on extended sentiment dictionary. IEEE access, 7, 43749-43762. XuG., YuZ., YaoH., LiF., MengY. & WuX. (2019). Chinese text sentiment analysis based on extended sentiment dictionary. IEEE access, 7, 43749-43762.Search in Google Scholar

[9]Van Atteveldt, W., Van der Velden, M. A., & Boukes, M. (2021). The validity of sentiment analysis: Comparing manual annotation, crowd-coding, dictionary approaches, and machine learning algorithms. Communication Methods and Measures, 15(2), 121-140. Van AtteveldtW., Van der VeldenM. A. & BoukesM. (2021). The validity of sentiment analysis: Comparing manual annotation, crowd-coding, dictionary approaches, and machine learning algorithms. Communication Methods and Measures, 15(2), 121-140.Search in Google Scholar

[10]Ain, Q. T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B., & Rehman, A. (2017). Sentiment analysis using deep learning techniques: a review. International Journal of Advanced Computer Science and Applications, 8(6). AinQ. T., AliM., RiazA., NoureenA., KamranM., HayatB. & RehmanA. (2017). Sentiment analysis using deep learning techniques: a review. International Journal of Advanced Computer Science and Applications, 8(6).Search in Google Scholar

[11]Bandhakavi, A., Wiratunga, N., Padmanabhan, D., & Massie, S. (2017). Lexicon based feature extraction for emotion text classification. Pattern recognition letters, 93, 133-142. BandhakaviA., WiratungaN., PadmanabhanD. & MassieS. (2017). Lexicon based feature extraction for emotion text classification. Pattern recognition letters, 93, 133-142.Search in Google Scholar

[12]Wadud, M. A. H., Mridha, M. F., & Rahman, M. M. (2022). Word embedding methods for word representation in deep learning for natural language processing. Iraqi Journal of Science, 1349-1361. WadudM. A. H., MridhaM. F. & RahmanM. M. (2022). Word embedding methods for word representation in deep learning for natural language processing. Iraqi Journal of Science, 1349-1361.Search in Google Scholar

[13]Abdullah, S. M. S. A., Ameen, S. Y. A., Sadeeq, M. A., & Zeebaree, S. (2021). Multimodal emotion recognition using deep learning. Journal of Applied Science and Technology Trends, 2(01), 73-79. AbdullahS. M. S. A., AmeenS. Y. A., SadeeqM. A. & ZeebareeS. (2021). Multimodal emotion recognition using deep learning. Journal of Applied Science and Technology Trends, 2(01), 73-79.Search in Google Scholar

[14]Ran Fang-ju, Xiong Chen-zhi, Lu Meng-yao & Yang Tian-qing. (2023). Research on university network public opinion sentiment analysis based on BERT and Bi-LSTM. (eds.)Baoshan University (China). Fang-juRan, Chen-zhiXiong, Meng-yaoLu & Tian-qingYang. (2023). Research on university network public opinion sentiment analysis based on BERT and Bi-LSTM. (eds.)Baoshan University (China).Search in Google Scholar

[15]Minchae Song, Hyunjung Park & Kyung-shik Shin. (2019). Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean. Information Processing and Management(3), 637-653. SongMinchae, ParkHyunjung & ShinKyung-shik. (2019). Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean. Information Processing and Management(3), 637-653.Search in Google Scholar

[16]Bing Liu. (2020). Text sentiment analysis based on CBOW model and deep learning in big data environment. Journal of Ambient Intelligence and Humanized Computing(2), 451-458. LiuBing. (2020). Text sentiment analysis based on CBOW model and deep learning in big data environment. Journal of Ambient Intelligence and Humanized Computing(2), 451-458.Search in Google Scholar

[17]Zeyu Xiong, Qiangqiang Shen, Yueshan Xiong, Yijie Wang & Weiz iLi. (2019). New Generation Model of Word Vector Representation Based on CBOW or Skip-Gram. Computers, Materials & Continua(1), 259-273. XiongZeyu, ShenQiangqiang, XiongYueshan, WangYijie & iLiWeiz. (2019). New Generation Model of Word Vector Representation Based on CBOW or Skip-Gram. Computers, Materials & Continua(1), 259-273.Search in Google Scholar