Multimodal corpus is a novel multimedia teaching tool in social development and educational reform process. It uses a range of multimedia components to build a wide-ranging English corpus and is mostly focused on computer and network technologies. The use of corpus in multimodal English-Chinese instruction is growing. The meaning, usage, and set of English and Chinese multimodality will be better understood with the aid of contemporary information technology, which will also enhance the initiative of autonomous learning. This paper builds a Multimodal English Corpus created on optimized-Constitutive Artificial Neural Network-Honey Badger Algorithm (MEC-CANN-HBA). The input data is collected via the dataset of Gutenberg Literary English Corpus (GLEC). The data are fed to pre-processing to remove the noise and enhance the input data utilizing Multivariate Fast Iterative Filtering (MFIF). The pre-processing output is given to the Feature extraction segment. The three significant features, such as text, audio and video are extracted based on Deep Wavelet Scattering Transform (DWST). After that, the extracted features are given to the multimodal fusion vector. The multimodal feature vectors are employed as the input data for categorization and to obtain the English poetry feature representation that integrates context characteristics. Finally, the features of output are used as the input data of Constitutive Artificial Neural Network (CANN) effectively categorizes as ideographic, phonetic, rhetorical and contextual. Honey Badger Algorithm (HBA) utilized for improving the weight parameter of CANN to check the classification of English poetry is current utterance. The proposed MEC-CANN-HBA approach attains 24.36%, 23.42%, 30%, 10.25% and 16.27% higher accuracy, and 26.61%, 28.50%, 23%, 18.33% and 21.24% greater precision rate, compared with existing methods, like Construction for Multiple Modal Corpus of College Students’ Spoken English Using Semantic Concepts (CMC-CSSE-SC), Construction of multimodal poetry translation corpus under AdaBoost method (CMPTC- AdaBoost),Construction with Application of English-Chinese Multimodal Emotional Corpus utilizing Artificial Intelligence (CECMC-AI) respectively.

[1] Baldry A. and Kantz D., Corpus-Assisted Approaches to Online Multimodal Discourse Analysis of Videos, Analysing Multimodality in Specialized Discourse Settings: Innovative Research Methods and Applications, Springer, 2022. DOI: 10.1057/9781137431738_4

[2] Brunner M. and Diemer S., “Multimodal Meaning Making: The Annotation of Nonverbal Elements in Multimodal Corpus Transcription,” Research in Corpus Linguistics, vol. 9, no. 1, pp. 63-88, 2021. https://doi.org/10.32714/ricl.09.01.05

[3] Cambria M., “Learning about Schools in the British Isles through a Video Corpus: Reflections on an Online Project for Digital Literacy and Multimodal Corpus Construction,” Journal of Elementary Education, vol. 16, pp. 117-135, 2023. DOI:10.18690/rei.16.Spec.Iss.2988

[4] Cang H. and Feng D., “Construction of English Corpus Oral Instant Translation Model Based on Internet of Things and Deep Learning of Information Security,” Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1507-1522, 2024. https://doi.org/10.3233/JCM-247183

[5] Carrió-Pastor M., “Teaching Multimodal Metadiscourse in Academic English as a Foreign Language,” Porta Linguarum, vol. 4, pp. 155- 172, 2022. https://doi.org/10.4000/lidil.10575

[6] Chakravarthi B., Muralidaran V., Priyadharshini R., and McCrae J., “Corpus Creation for Sentiment Analysis in Code-Mixed Tamil- English Text,” arXiv Preprint, vol. arXiv:2006.00206, pp. 1-9, 2020. https://doi.org/10.48550/arXiv.2006.00206

[7] Chen J., Liu S., and Shao D., “The Construction and Practice of a Multimodal Corpus for the Ideological and Political Education in Aerospace‐ themed College English Curriculum,” International Journal of Social Science and Education Research, vol. 7, no. 5, pp. 258-263, 2024. https://doi.org/10.6918/IJOSSER.202405_7(5).0 036

[8] Chen Y., Harrison S., Stevens M., and Zhou Q., “Developing a Multimodal Corpus of L2 Academic English from an English Medium of Instruction University in China,” Corpora, vol. 19, no. 1, pp.1-15, 2024.

[9] Chen Z., Subtitling as Multimodal Representation: A Corpus-Based Experimental Approach to Text-Image Relations, Ph.D. Thesis, The Hong Kong Polytechnic University, 2024. https://theses.lib.polyu.edu.hk/bitstream/200/128 21/3/7272.pdf

[10] Deng L., “Culture’s Representation in Van Gulik’s Transcreated Novel the Chinese Maze Murders: A Multimodal Corpus Approach,” SAGE Open, vol. 14, no. 1, pp. 1-12, 2024. https://doi.org/10.1177/21582440231223481

[11] Dong S., “Intelligent English Teaching Prediction System Based on SVM and Heterogeneous Multimodal Target Recognition,” 408 The International Arab Journal of Information Technology, Vol. 22, No. 2, March 2025 Journal of Intelligent and Fuzzy Systems, vol. 38, no. 6, pp. 7145-7154, 2020. DOI:10.3233/JIFS- 179792

[12] Eijk L., Rasenberg M., Arnese F., Blokpoel M., Dingemanse M., Doeller C., Ernestus M., Holler J., Milivojevic B., Özyürek A., and Pou W., “The CABB Dataset: A Multimodal Corpus of Communicative Interactions for Behavioural and Neural Analyses,” NeuroImage, vol. 264, pp. 119734, 2022. https://doi.org/10.1016/j.neuroimage.2022.1197 34

[13] Harb H., Multimodal Emotion Recognition Using Temporal Convolutional Networks, Master Thesis, University of Ottawa, 2023. https://ruor.uottawa.ca/items/155adedf-62a6- 4ef4-a254-08ed190d1d75

[14] Hashim F., Houssein E., Hussain K., Mabrouk M., and Al-Atabany W., “Honey Badger Algorithm: New Metaheuristic Algorithm for Solving Optimization Problems,” Mathematics and Computers in Simulation, vol. 192, pp. 84- 110, 2022. https://doi.org/10.1016/j.matcom.2021.08.013

[15] Hoemann K. and Leuven K., Modeling the Meaning of Emotion Words Using Multimodal Features of Real-World Contexts, https://iclc16.github.io/abstracts/ICLC16_paper_ 486.pdf, Last Visited, 2024.

[16] Hu G., “The Model Construction of Human- Computer Interaction Teaching in College English Multimodal Teaching,” Frontiers in Educational Research, vol. 7, no. 2, pp. 178-188, 2024. https://doi.org/10.25236/FER.2024.070228

[17] Huang L., “Toward Multimodal Corpus Pragmatics: Rationale, Case, And Agenda,” Digital Scholarship in the Humanities, vol. 36, no. 1, pp. 101-114, 2021. https://doi.org/10.1093/llc/fqz080

[18] Khushaba R. and Hill A., “Radar-Based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter Vs. Millimeter Wave Units,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2016-2022, 2022. DOI:10.1109/LRA.2022.3143200

[19] Kumari R., Ashok N., Agrawal P., Ghosal T., and Ekbal A., “Identifying Multimodal Misinformation Leveraging Novelty Detection and Emotion Recognition,” Journal of Intelligent Information Systems, pp. 1-22, 2023. DOI:10.1007/s10844-023-00789-x

[20] Li S. and Okada S., “Interpretable Multimodal Sentiment Analysis Based on Textual Modality Descriptions by Using Large-Scale Language Models,” arXiv Preprint, vol. arXiv:2305.06162, pp. 1-10, 2023. https://doi.org/10.48550/arXiv.2305.06162

[21] Li S., “Construction of a Multimodal Poetry Translation Corpus Based on AdaBoost Model,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, pp. 1-15, 2023. https://doi.org/10.2478/amns.2023.1.00102

[22] Liang C., Xu J., Zhao J., Chen Y., and Huang J., “Deep Learning-Based Construction and Processing of Multimodal Corpus for IoT Devices in Mobile Edge Computing,” Computational Intelligence and Neuroscience, vol. 2022, no. 241310, pp. 1-10, 2022. https://doi.org/10.1155/2022/2241310

[23] Lin Y., Chi Y., Han H., Han M., and Guo Y., “Multimodal Orthodontic Corpus Construction Based on Semantic Tag Classification Method,” Neural Processing Letters, vol. 54, no. 4, pp. 2817-2830, 2022. https://doi.org/10.1007/s11063-021-10558-y

[24] Linka K., Hillgärtner M., Abdolazizi K., Aydin R., Itskov M., and Cyron C., “Constitutive Artificial Neural Networks: A Fast and General Approach to Predictive Data-Driven Constitutive Modeling by Deep Learning,” Journal of Computational Physics, vol. 429, pp. 110010, 2021. https://doi.org/10.1016/j.jcp.2020.110010

[25] Merritt A., Chu C., and Arase Y., “A Corpus for English-Japanese Multimodal Neural Machine Translation with Comparable Sentences,” arXiv Preprint, vol. arXiv:2010.08725, pp. 1-6, 2020. https://doi.org/10.48550/arXiv.2010.08725

[26] Nan C., “Multidimensional Psychological Model Construction of Public English Teaching Based on Deep Learning from Multimodal Perspective,” Mobile Information Systems, vol. 2022, no. 1, pp. 1653452, 2022. https://doi.org/10.1155/2022/1653452

[27] Pinto S. and Mubaraki A., “Multimodal Corpus Analysis of Subtitling: The Case of Non-Standard Varieties,” Target: International Journal of Translation Studies, vol. 32, no. 3, pp. 389-419, 2020. https://doi.org/10.1075/target.18085.ram

[28] Reece A., Cooney G., Bull P., Chung C., Dawson B., Fitzpatrick C., Glazer T., Knox D., Liebscher A., and Marin S., “Advancing an Interdisciplinary Science of Conversation: Insights from a Large Multimodal Corpus of Human Speech,” arXiv Preprint, vol. arXiv:2203.00674, pp. 1-116, 2022. https://doi.org/10.48550/arXiv.2203.00674

[29] Rodríguez-Peñarroja M., “Corpus Pragmatics and Multimodality: Compiling an ad-hoc Multimodal Corpus for EFL Pragmatics Teaching,” International Journal of Instruction, vol. 14, no. 1, pp. 927-946, 2020. DOI:10.29333/iji.2021.14155a

[30] Siever C., and Siever T., Shifts Towards Image- Centricity in Contemporary Multimodal Constitutive Artificial Neural Network for the Construction of an English Multimodal Corpus 409 Practices, Routledge, pp. 177-203, 2020.

[31] Singh N. and Kapoor R., “Multi-modal Expression Detection (MED): A Cutting-Edge Review of Current Trends,” Challenges and Solutions Engineering Applications of Artificial Intelligence, vol. 125, pp. 106661, 2023. https://doi.org/10.1016/j.engappai.2023.106661

[32] Stevens M., Chen Y., and Harrison S., Variation in Time and Space, De Gruyter, 2020. https://doi.org/10.1515/9783110604719-015

[33] Tian M., “Construction of Computer English Corpus Assisted by Internet of Things Information Perception and Interaction Technology,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, pp. 6803802, 2022. https://doi.org/10.1155/2022/6803802

[34] Tian X., “Construction of a Multimodal Corpus of College Students’ Spoken English Based on Semantic Concepts,” Mobile Information Systems, vol. 2022, pp. 1-10, 2022. https://doi.org/10.1155/2022/5270408

[35] Wang Y., Wang Z., Kang X., and Luo Y., “A Novel Interpretable Model Ensemble Multivariate Fast Iterative Filtering and Temporal Fusion Transform for Carbon Price Forecasting,” Energy Science and Engineering, vol. 11, no. 3, pp. 1148-1179, 2023. https://doi.org/10.1002/ese3.1380

[36] Yang H., Si Z., Zhao Y., Liu J., Wu Y., and Qin B. “MACSA: A Multimodal Aspect-Category Sentiment Analysis Dataset with Multimodal Fine-Grained Aligned Annotations,” arXiv Preprint, vol. arXiv:2206.13969, pp.1-10, 2022. https://doi.org/10.48550/arXiv.2206.13969

[37] Zadeh A., Zellers R., Pincus E., and Morency L., “MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos,” arXiv Preprint, vol. arXiv:1606.06259, pp. 1-10, 2016. https://doi.org/10.48550/arXiv.1606.06259

[38] Zeng Z. and Li Y., “Multi-modal Chinese Text Emotion Metaphor Computation Based on Mutual Information and Information Entropy,” ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 8, pp. 1-18, 2023. https://doi.org/10.1145/3605211

[39] Zhang L., “Ideological and Political Empowering English Teaching: Ideological Education Based on Artificial Intelligence in Classroom Emotion Recognition,” International Journal of Computer Applications in Technology, vol. 71, no. 3, pp. 265-271, 2023. https://doi.org/10.1504/IJCAT.2023.132103

[40] Zhou W. and Gao B., “Construction and Application of English-Chinese Multimodal Emotional Corpus Based on Artificial Intelligence,” International Journal of Human- Computer Interaction, pp. 1-12. 2023.

[41] Zhu W., Hessel J., Awadalla A., Gadre S., Dodge J., Fang A., Yu Y., Schmidt L., Wang W., and Choi Y., “Multimodal c4: An Open, Billion-Scale Corpus of Images Interleaved with Text,” in Proceedings of the 37th Conference on Neural Information Processing Systems, New Orleans, pp. 1-17, 2023. https://doi.org/10.48550/arXiv.2304.06939