Using TextCNN Method to Extract Sentiment Tendency and Material Perception Mapping in Leather Product Consumer Reviews
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Abstract
Accurately identifying the relationship between consumer sentiment and material perception is essential for intelligent product evaluation and data-driven manufacturing optimization. In engineering scenarios involving electromagnetic sensing and smart inspection systems, reliable semantic understanding of material-related information can provide complementary support for multimodal perception and decision-making. To address the problems of mixed sentiment expressions, long-range semantic dependencies, and ambiguous sentiment-material associations in leather product reviews, this study proposes an improved TextCNN framework integrating gated attention and multi-task joint learning. The proposed method dynamically filters salient semantic features through a gating mechanism and employs global attention weighting to reconstruct long-distance contextual relationships while simultaneously optimizing sentiment classification and material perception mapping using shared semantic representations. Experimental results demonstrate that the proposed model achieves a Macro-F1 score of 89.4% for sentiment recognition, an average semantic association accuracy of 79.9%, and a mapping coverage of 81.6%, outperforming representative baseline methods. The framework effectively enhances the interpretability of sentiment-material coupling and provides a practical semantic analysis strategy for intelligent material evaluation, multimodal information fusion, and engineering applications associated with electromagnetic sensing and advanced manufacturing systems.
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References
I. D’Adamo, M. Gagliarducci, M. Iannilli, and V. Mangani, “Fashion Wears Sustainable Leather: A So-cial and Strategic Analysis Toward Sustainable Production and Consumption Goals,” Sustainability, vol. 16, no. 22, pp. 9971-10005, 2024, doi: 10.3390/su16229971.
D. Rocklage M and H. Fazio R, “The enhancing versus backfiring effects of positive emotion in con-sumer reviews,” Journal of Marketing Research, vol. 57, no. 2, pp. 332-352, 2020, doi: 10.1177/0022243719892594.
B. Lis and M. Fischer, “Analyzing different types of negative online consumer reviews,” Journal of Product & Brand Management, vol. 29, no. 5, pp. 637-653, 2020, doi: 10.1108/JPBM-05-2018-1876.
K. Sharma, S. Trott, S. Sahadev, and R. Singh, “Emotions and consumer behaviour: A review and re-search agenda,” International Journal of Consumer Studies, vol. 47, no. 6, pp. 2396-2416, 2023, doi: 10.1111/ijcs.12937.
J. Alantari H, S. Currim I, Y. Deng, and S. Singh, “An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews,” International Journal of Research in Marketing, vol. 39, no. 1, pp. 1-19, 2022, doi: 10.1016/j.ijresmar.2021.10.011.
V. Srivastava, D. Kalro A, G. Raizada, & Sharma, and D, “Beyond stars: role of discrete emotions on online consumer review helpfulness,” Journal of Marketing Theory and Practice, vol. 32, no. 2, pp. 137-157, 2024, doi: 10.1080/10696679.2022.2148111.
P. Nandwani and R. Verma, “A review on sentiment analysis and emotion detection from text,” Social network analysis and mining, vol. 11, no. 1, pp. 81-99, 2021, doi: 10.1007/s13278-021-00776-6.
A. Ramadhan F, P. Ruslan R R, and A. Zahra, “Sentiment analysis of e-commerce product reviews for content interaction using machine learning,” Cakrawala Repositori IMWI, vol. 6, no. 1, pp. 207-220, 2023, doi: 10.52851/cakrawala.v6i1.219.
K. Kardaras D, C. Troussas, G. Barbounaki S, P. Tselenti, and K. & Armyras, “A Fuzzy Synthetic Evalua-tion Approach to assess usefulness of Tourism Reviews by considering bias identified in Sentiments and Articulacy,” Information, vol. 15, no. 4, pp. 236-254, 2024, doi: 10.3390/info15040236.
S. Mitra and M. Jenamani, “Helpfulness of online consumer reviews: A multi-perspective approach,” Information Processing & Management, vol. 58, no. 3, pp. 1-17, 2021, doi: 10.1016/j.ipm.2021.102538.
H. Almaqtari, F. Zeng, and A. Mohammed, “Enhancing Arabic Sentiment Analysis of Consumer Re-views: Machine Learning and Deep Learning Methods Based on NLP,” Algorithms, vol. 17, no. 11, pp. 495-510, 2024, doi: 10.3390/a17110495.
M. Yang, “An effective emotional analysis method of consumer comment text based on AL-BERT-ATBiFRU-CNN,” International Journal of Information Technologies and Systems Approach (IJITSA), vol. 16, no. 2, pp. 1-12, 2022, doi: 10.4018/IJITSA.324100.
S. Salim, Z. Iqbal, and J. Iqbal, “Emotion classification through product consumer reviews,” Pakistan Journal of Engineering and Technology, vol. 4, no. 4, pp. 35-40, 2021, doi: 10.51846/vol4iss4pp35-40.
A. Alamsyah and D. Girawan N, “Improving clothing product quality and reducing waste based on consumer review using RoBERTa and BERTopic language model,” Big Data and Cognitive Compu-ting, vol. 7, no. 4, pp. 168-188, 2023, doi: 10.3390/bdcc7040168.
J. Choi, “Material selection: Exploring the reliability of material perception data and its relevance to materials,” Metals and Materials International, vol. 27, no. 6, pp. 1599-1604, 2021, doi: 10.1007/s12540-019-00574-y.
H. Liu, “Online review analysis on various networks’ consumer feedback using deep learning,” IET networks, vol. 11, no. 6, pp. 234-244, 2022, doi: 10.1049/ntw2.12045.
Z. Labd, S. Bahassine, K. Housni, H. Aadi F Z A, & Benabbes, and K, “Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representa-tion: a comparative study,” International Journal of Electrical & Computer Engineering (2088-8708), vol. 14, no. 1, pp. 589-599, 2024, doi: 10.11591/ijece.v14i1.pp589-599.
M. Mohammed S, K. Jacksi, and S. Zeebaree, “A state-of-the-art survey on semantic similarity for doc-ument clustering using GloVe and density-based algorithms,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 1, pp. 552-562, 2021, doi: 10.11591/ijeecs.v22.i1.pp552-562.
A. Fellah, A. Zahaf, and A. Elci, “Semantic similarity measure using a combination of word2vec and wordnet models,” Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 12, no. 2, pp. 455-464, 2024, doi: 10.52549/ijeei.v12i2.5114.
K. Babic, F. Guerra, S. Martincic-Ipsic, and A. & Mestrovc, “A comparison of approaches for measuring the semantic similarity of short texts based on word embeddings,” Journal of information and organiza-tional sciences, vol. 44, no. 2, pp. 231-246, 2020, doi: 10.31341/jios.44.2.2.
Q. Li, X. Li, Y. Du, and X. & Chen, “A new sentiment-enhanced word embedding method for sentiment analysis,” Applied Sciences, vol. 12, no. 20, pp. 10236-10253, 2022, doi: 10.3390/app122010236.
H. Yang, S. Zhang, H. Shen, G. Zhang, Deng, X, J. Xiong, et al., “A multi-layer feature fusion model based on convolu tion and attention mechanisms for text classification,” Applied Sciences, vol. 13, no. 14, pp. 8550-8568, 2023, doi: 10.3390/app13148550.
J. Mutinda, W. Mwangi, and G. Okeyo, “Sentiment analysis of text reviews using lexicon-enhanced bert embedding (LeBERT) model with convolutional neural network,” Applied Sciences, vol. 13, no. 3, pp. 1445-1458, 2023, doi: 10.3390/app13031445.
K. Agbesi V, W. Chen, B. Yussif S, C. Ukwuoma C, H. Gu Y, and A. & Al-Antari M, “MuTCELM: An optimal multi-TextCNN-based ensemble learning for text classification,” Heliyon, vol. 10, no. 19, pp. 1-21, 2024, doi: 10.1016/j.heliyon.2024.e38515.