Sentiment Propensity Analysis of a Multimodal Chinese Corpus Using Fuzzy Logic

[1] Liu, X., Chen, Q., Deng, C., Zeng, H., Chen, J., Li, D., & Tang, B. (2018, August). Lcqmc: A large-scale chinese question matching corpus. In Proceedings of the 27th international conference on computational linguistics (pp. 1952-1962). Search in Google Scholar

[2] Yuan, S., Zhao, H., Du, Z., Ding, M., Liu, X., Cen, Y., ... & Tang, J. (2021). Wudaocorpora: A super large-scale chinese corpora for pre-training language models. AI Open, 2, 65-68. Search in Google Scholar

[3] Chen, J., Chen, Q., Liu, X., Yang, H., Lu, D., & Tang, B. (2018). The bq corpus: A large-scale domain-specific chinese corpus for sentence semantic equivalence identification. In Proceedings of the 2018 conference on empirical methods in natural language processing (pp. 4946-4951). Search in Google Scholar

[4] Li, Y., Cao, J., Cong, X., Zhang, Z., Yu, B., Zhu, H., & Liu, T. (2022, May). Enhancing Chinese pre-trained language model via heterogeneous linguistics graph. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1986-1996). Search in Google Scholar

[5] Huang, L. (2018). Issues on multimodal corpus of Chinese speech acts: A case in multimodal pragmatics. Digital Scholarship in the Humanities, 33(2), 316-326. Search in Google Scholar

[6] Gao, F., & Wang, B. (2018). Multimodal corpus approach to dialogue interpreting studies in the Chinese context: Towards a multi-layer analytic framework. The Interpreters Newsletter, 2017(22), 17-38. Search in Google Scholar

[7] Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological science in the public interest, 20(1), 1-68. Search in Google Scholar

[8] Grandey, A. A., & Melloy, R. C. (2017). The state of the heart: Emotional labor as emotion regulation reviewed and revised. Journal of occupational health psychology, 22(3), 407. Search in Google Scholar

[9] Desmet, P. (2018). Measuring emotion: Development and application of an instrument to measure emotional responses to products. Funology 2: From Usability to Enjoyment, 391-404. Search in Google Scholar

[10] Pellert, M., Schweighofer, S., & Garcia, D. (2020). The individual dynamics of affective expression on social media. EPJ Data Science, 9(1), 1. Search in Google Scholar

[11] Queiroz, T., Monteiro, C., Carvalho, L., & François, K. (2017). Interpretation of statistical data: The importance of affective expressions. Statistics Education Research Journal, 16(1), 163-180. Search in Google Scholar

[12] Hur, K., Kim, T. T., Karatepe, O. M., & Lee, G. (2017). An exploration of the factors influencing social media continuance usage and information sharing intentions among Korean travellers. Tourism management, 63, 170-178. Search in Google Scholar

[13] Hasan, M. M., Al Amin, M., Arefin, M. S., & Mostafa, T. (2024). Green consumers’ behavioral intention and loyalty to use mobile organic food delivery applications: the role of social supports, sustainability perceptions, and religious consciousness. Environment, Development and Sustainability, 26(6), 15953-16003. Search in Google Scholar

[14] Chen, S. (2023). Analysis of social media’s online expression strategy for social hot spots based on the perspective of mass communication-Take “Bilibili” as an example. In SHS Web of Conferences (Vol. 155, p. 02001). EDP Sciences. Search in Google Scholar

[15] Gu, M., Guo, H., Zhuang, J., Du, Y., & Qian, L. (2022). Social media user behavior and emotions during crisis events. International journal of environmental research and public health, 19(9), 5197. Search in Google Scholar

[16] Zhang, M. (2023). Analysis of Differences in Comments of Internet Users under Hot Internet Events: The Case of the Nuclear Sewage Discharge Incident in Japan. Journal of Sociology and Ethnology, 5(11), 14-23. Search in Google Scholar

[17] Katta, P., & Hegde, N. P. (2019). A Hybrid Adaptive Neuro-Fuzzy Interface and Support Vector Machine Based Sentiment Analysis on Political Twitter Data. International Journal of Intelligent Engineering & Systems, 12(1). Search in Google Scholar

[18] Bonny, E. L., & Liang, D. (2023). Exploring sentiment polarity of p2p guests through online user reviews: A multimodal fuzzy approach. IEEE Transactions on Engineering Management. Search in Google Scholar

[19] Jaehak Yu, Yangseo Choi, Kijong Koo & Daesung Moon. (2024). A novel approach for application classification with encrypted traffic using BERT and packet headers. Computer Networks110747-110747. Search in Google Scholar

[20] Jiacai Han & Pan Zeng. (2024). Short-term power load forecasting based on hybrid feature extraction and parallel BiLSTM network. Computers and Electrical Engineering(PB),109631-109631. Search in Google Scholar

[21] Ali Ghous, Afzal Ayesha, Sheikh Umber & Nabeel Muhammad. (2023). Multi-criteria group decision-making based on the combination of dual hesitant fuzzy sets with soft expert sets for the prediction of a local election scenario. Granular Computing(6),2039-2066. Search in Google Scholar

[22] Yuchen Wu, Ziwen Wang & Lei Zhang. (2024). A Novel Clark Distance-Based Decision-Making Algorithm on Intuitionistic Fuzzy Sets. Electronics(17),3481-3481. Search in Google Scholar

[23] Huang Yang. (2024). Developing a fuzzy comprehensive assessment model for English translation for college studentso. Journal of Computational Methods in Sciences and Engineering(3),1709-1725. Search in Google Scholar