Innovative Label Embedding for Food Safety Comment Classification: Fusion of Self-Semantic and Self-Knowledge Features

Yiming Zhang, Haozheng Liu, Jiaming Feng, Xu Zhang


Food safety comment classification represents a specialized task within the realm of text classification. The objective is to efficiently identify a large volume of food safety comments, aiding relevant authorities in timely food analysis and safety alerts. Traditional methods typically employ one-hot encoding for label processing. However, in real-world situations, classified labels often convey valuable semantic information and guidance. This paper introduces an innovative approach to enhance the classification performance of food safety comments by embedding label information. Initially, we extracted generic sentiment pivot words from various classification labels as label description information. Subsequently, we employ a joint embedding approach to integrate this label description information into the text. This process will pool the expressions of the pivot word into the corresponding sentiment labels in the known domains after averaging to get the embedded expression. This aims to acquire highly detailed self-semantic feature vectors and self-knowledge feature vectors that are integrated with labeled descriptive information. Then, feed the semantic representation of comments and the word-embedded representation of labeled description information into a time-step-based multilayer Bi-LSTM and a step-based multilayer CNN, respectively. Ultimately, we concatenate these two feature vectors to facilitate matching, thereby fusing the self-semantic and self-knowledge features of labeled description information to train a classification model for food safety comments. Experimental results on the food safety comment dataset showcase a noteworthy improvement of 1.74% and 1.27% in Macro_Precision and Macro_F1 metrics, respectively, compared to BERT, BERT-RNN, and BERT-CNN. Through extensive ablation experiments and additional studies, our method effectively embeds labeling information, demonstrating a clear advantage over traditional methods in the task of classifying food safety comments.


Doi: 10.28991/HIJ-2024-05-01-013

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BERT; Label Embedding; Siamese Network; Pre-trained Models; Short Text Classification; Food Safety Lead Discovery.


Seo, S., Almanza, B., Miao, L., & Behnke, C. (2015). The Effect of Social Media Comments on Consumers’ Responses to Food Safety Information. Journal of Foodservice Business Research, 18(2), 111–131. doi:10.1080/15378020.2015.1029384.

Lobb, A. (2005). Consumer trust, risk and food safety: A review. Food Economics-Acta Agriculturae Scandinavica, Section C, 2(1), 3-12. doi:10.1080/16507540510033424.

Jin, C., Bouzembrak, Y., Zhou, J., Liang, Q., Van Den Bulk, L. M., Gavai, A., ... & Marvin, H. J. (2020). Big Data in food safety-A review. Current Opinion in Food Science, 36, 24-32. doi:10.1016/j.cofs.2020.11.006.

Wang, J., & Yue, H. (2017). Food safety pre-warning system based on data mining for a sustainable food supply chain. Food Control, 73, 223–229. doi:10.1016/j.foodcont.2016.09.048.

Geng, Z., Shang, D., Han, Y., & Zhong, Y. (2019). Early warning modeling and analysis based on a deep radial basis function neural network integrating an analytic hierarchy process: A case study for food safety. Food Control, 96, 329–342. doi:10.1016/j.foodcont.2018.09.027.

Van de Brug, F. J., Lucas Luijckx, N. B., Cnossen, H. J., & Houben, G. F. (2014). Early signals for emerging food safety risks: From past cases to future identification. Food Control, 39(1), 75–86. doi:10.1016/j.foodcont.2013.10.038.

Huang, Y., Wang, X., Wang, R., & Min, J. (2022). Analysis and Recognition of Food Safety Problems in Online Ordering Based on Reviews Text Mining. Wireless Communications and Mobile Computing, 2022, 1–15. doi:10.1155/2022/4209732.

Li, Y., Gao, X., Du, M., He, R., Yang, S., & Xiong, J. (2020). What causes different sentiment classification on social network services? Evidence from weibo with genetically modified food in China. Sustainability (Switzerland), 12(4), 1345. doi:10.3390/su12041345.

Wang, S., & Manning, C. D. (2012). Baselines and bigrams: Simple, good sentiment and topic classification. 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference, 2, 90–94.

Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014). A convolutional neural network for modelling sentences. 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference, 1, 655–665. doi:10.3115/v1/p14-1062.

Zhang, H., Xiao, L., Chen, W., Wang, Y., & Jin, Y. (2018). Multi-task label embedding for text classification. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP, 4545–4553. doi:10.18653/v1/d18-1484.

Tan, C., Ren, Y., & Wang, C. (2023). An adaptive convolution with label embedding for text classification. Applied Intelligence, 53(1), 804–812. doi:10.1007/s10489-021-02702-x.

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. doi:10.1162/neco.1997.9.8.1735.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1, 4171–4186. doi:10.48550/arXiv.1810.04805.

Miyazaki, T., Makino, K., Takei, Y., Okamoto, H., & Goto, J. (2019). Label embedding using hierarchical structure of labels for twitter classification. EMNLP-IJCNLP - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 6317–6322. doi:10.18653/v1/d19-1660.

Zhang, K., Wu, L., Lv, G., Chen, E., Ruan, S., Liu, J., Zhang, Z., Zhou, J., & Wang, M. (2023). Description-Enhanced Label Embedding Contrastive Learning for Text Classification. IEEE Transactions on Neural Networks and Learning Systems, 1-14. doi:10.1109/TNNLS.2023.3282020.

Hambrick, D. Z., & Meinz, E. J. (2011). Limits on the predictive power of domain-specific experience and knowledge in skilled performance. Current Directions in Psychological Science, 20(5), 275–279. doi:10.1177/0963721411422061.

Akata, Z., Perronnin, F., Harchaoui, Z., & Schmid, C. (2016). Label-Embedding for Image Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7), 1425–1438. doi:10.1109/TPAMI.2015.2487986.

Qu, X., Che, H., Huang, J., Xu, L., & Zheng, X. (2023). Multi-layered semantic representation network for multi-label image classification. International Journal of Machine Learning and Cybernetics, 14(10), 3427–3435. doi:10.1007/s13042-023-01841-6.

Rodriguez-Serrano, J. A., & Perronnin, F. (2013). Label embedding for text recognition. BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference, 1-12. doi:10.5244/C.27.5.

Frome, A., Corrado, G. S., Shlens, J., Bengio, S., Dean, J., Ranzato, M. A., & Mikolov, T. (2013). Devise: A deep visual-semantic embedding model. Advances in Neural Information Processing Systems, 26, 2121-2129.

He, S., Guo, T., Dai, T., Qiao, R., Shu, X., Ren, B., & Xia, S. T. (2023). Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 37(1), 808–816. doi:10.1609/aaai.v37i1.25159.

Palatucci, M., Pomerleau, D., Hinton, G., & Mitchell, T. M. (2009). Zero-shot learning with semantic output codes. Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference, 22, 1410–1418.

Zhang, H., Meng, X., Cao, W., Liu, Y., Ming, Z., & Yang, J. (2023). Graph embedding based multi-label Zero-shot Learning. Neural Networks, 167, 129–140. doi:10.1016/j.neunet.2023.08.023.

Tang, J., Qu, M., & Mei, Q. (2015). PTE: Predictive text embedding through large-scale heterogeneous text networks. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 1165-1174. doi:10.1145/2783258.2783307.

Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., ... & He, L. (2020). A survey on text classification: From shallow to deep learning. arXiv preprint, arXiv:2008.00364. doi:10.48550/arXiv.2008.00364

Chen, X., Qiu, X., Zhu, C., Wu, S., & Huang, X. (2015). Sentence modeling with gated recursive neural network. Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing, 793–798. doi:10.18653/v1/d15-1092.

Zagoruyko, S., & Komodakis, N. (2015). Learning to compare image patches via convolutional neural networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 4353–4361. doi:10.1109/CVPR.2015.7299064.

Yu, J., & Jiang, J. (2016). Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification. EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings, 236–246. doi:10.18653/v1/d16-1023.

Church, K. W. (2017). Word2Vec. Natural Language Engineering, 23(1), 155-162. doi:10.1017/S1351324916000334.

Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. EMNLP - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 1532–1543. doi:10.3115/v1/d14-1162.

Cui, Y., Che, W., Liu, T., Qin, B., & Yang, Z. (2021). Pre-Training with Whole Word Masking for Chinese BERT. IEEE/ACM Transactions on Audio Speech and Language Processing, 29, 3504–3514. doi:10.1109/TASLP.2021.3124365.

Sun, Y., Wang, S., Li, Y., Feng, S., Chen, X., Zhang, H., ... & Wu, H. (2019). Ernie: Enhanced representation through knowledge integration. arXiv preprint arXiv:1904.09223. doi:10.48550/arXiv.1904.09223.

Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. arXiv preprint, arXiv:1412.6980. doi:10.48550/arXiv.1412.6980.

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DOI: 10.28991/HIJ-2024-05-01-013


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