PoultryLeX-Net: Domain-Adaptive Dual-Stream Transformer Architecture for Large-Scale Poultry Stakeholder Modeling
arXiv:2603.09991v1 Announce Type: cross 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
arXiv:2603.09991v1 Announce Type: cross 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.
Executive Summary
This article presents PoultryLeX-Net, a novel transformer architecture designed for fine-grained sentiment analysis in poultry-related text. The proposed framework integrates sentiment classification, topic modeling, and contextual representation learning through domain-specific embeddings and gated cross-attention mechanisms. PoultryLeX-Net was evaluated against multiple baseline models and consistently outperformed them, achieving high accuracy and F1 scores. The study demonstrates the effectiveness of domain adaptation and dual-stream attention in improving sentiment classification. The proposed framework has the potential to provide scalable intelligence for poultry production decision support. However, the study's focus on a specific industry and dataset may limit its generalizability.
Key Points
- ▸ PoultryLeX-Net is a domain-adaptive dual-stream transformer architecture for fine-grained sentiment analysis in poultry-related text.
- ▸ The proposed framework integrates sentiment classification, topic modeling, and contextual representation learning.
- ▸ PoultryLeX-Net outperformed multiple baseline models, achieving high accuracy and F1 scores.
Merits
Strength in Domain Adaptation
PoultryLeX-Net's domain adaptation capabilities enable it to effectively handle domain-specific terminology and sentiment cues, resulting in improved sentiment classification performance.
Dual-Stream Attention Mechanism
The gated cross-attention mechanism allows PoultryLeX-Net to capture both local and long-range semantic dependencies, enhancing its contextual understanding and sentiment analysis capabilities.
Demerits
Limited Generalizability
The study's focus on a specific industry and dataset may limit the applicability of PoultryLeX-Net to other domains and datasets, requiring further evaluation and adaptation.
Dependence on High-Quality Training Data
PoultryLeX-Net's performance is heavily dependent on the quality and quantity of the training data, which may not always be available or representative of the real-world scenario.
Expert Commentary
The proposed PoultryLeX-Net framework demonstrates a significant improvement in sentiment classification performance, particularly in the poultry industry. However, the study's limitations, such as dependence on high-quality training data and limited generalizability, require careful consideration. Future work should focus on adapting PoultryLeX-Net to other domains and datasets, as well as exploring its potential applications in other industries. Additionally, the study's policy implications highlight the need for further research on the impact of sentiment analysis on industry practices and policy decisions.
Recommendations
- ✓ Future studies should focus on adapting PoultryLeX-Net to other domains and datasets to evaluate its generalizability and potential applications.
- ✓ Researchers should explore the potential applications of PoultryLeX-Net in other industries, such as agriculture, food processing, and animal welfare.