Abstract
The rapid growth of the global poultry industry, driven by rising demand for affordable animal protein, has intensified public discourse surrounding production practices, housing, management, animal welfare, and supply-chain transparency. Social media platforms such as X (formerly Twitter) generate large volumes of unstructured textual data that capture stakeholder sentiment across the poultry industry. Extracting accurate sentiment signals from this domain-specific discourse remains challenging due to contextual ambiguity, linguistic variability, and limited domain awareness in general-purpose language models. This study presents PoultryLeX-Net, a lexicon-enhanced, domain-adaptive dual-stream transformer framework for fine-grained sentiment analysis in poultry-related text. The proposed architecture integrates sentiment classification, topic modeling, and contextual representation learning through domain-specific embeddings and gated cross-attention mechanisms. A lexicon-guided stream captures poultry-specific terminology and sentiment cues, while contextual stream models long-range semantic dependencies. Latent Dirichlet Allocation is employed to identify dominant thematic structures associated with production management and welfare-related discussions, providing complementary interpretability to sentiment predictions. PoultryLeX-Net was evaluated against multiple baseline models, including convolutional neural network and pre-trained transformer architectures such as DistilBERT and RoBERTa. PoultryLeX-Net consistently outperformed all baselines, achieving an accuracy of 97.35%, an F1 score of 96.67%, and an area under the receiver operating characteristic curve (AUC-ROC) of 99.61% across sentiment classification tasks. Overall, domain adaptation and dual-stream attention markedly improve sentiment classification, enabling scalable intelligence for poultry production decision support.
Abstract (translated)
全球家禽产业的快速增长，由对负担得起的动物蛋白质需求上升所推动，已加剧了围绕生产实践、饲养环境、管理方式、动物福利以及供应链透明度等议题的公众讨论。社交媒体平台如X（前身为Twitter）产生了大量非结构化的文本数据，这些数据捕捉到了家禽行业中利益相关者的观点和情绪。然而，从这个特定领域的对话中提取准确的情绪信号仍然具有挑战性，这主要是由于语境模糊、语言变异以及通用语言模型在领域知识方面的限制。 这项研究提出了PoultryLeX-Net，这是一种增强词汇表的、适应性强的双通道变压器框架，用于家禽相关文本中的细粒度情绪分析。所提出的架构通过特定领域的嵌入和门控交叉注意力机制整合了情绪分类、主题建模以及上下文表示学习。一个由词汇表引导的流捕捉与家禽相关的专业术语及情绪线索，而另一个上下文流则模型化长距离语义依赖关系。使用潜在狄利克雷分配来识别与生产管理和福利相关讨论中的主要主题结构，从而为情绪预测提供额外的解释能力。 在多项基础模型对比测试中评估了PoultryLeX-Net，包括卷积神经网络以及预训练变压器架构如DistilBERT和RoBERTa。PoultryLeX-Net在整个情绪分类任务上始终优于所有基准，实现了97.35%的准确率、96.67%的F1分数以及AUC-ROC下面积为99.61%。 总的来说，领域适应性和双通道注意力显著改善了情绪分类效果，使得基于家禽生产的决策支持能够实现可扩展智能。
URL
https://arxiv.org/abs/2603.09991
