PoultryLeX-Net: Domain-Adaptive Dual-Stream Transformer Architecture for Large-Scale Poultry Stakeholder Modeling
Stephen Afrifa, Biswash Khatiwada, Kapalik Khanal, Sanjay Shah, Lingjuan Wang-Li, and Ramesh Bahadur Bist

TL;DR
PoultryLeX-Net is a novel dual-stream transformer architecture that enhances sentiment analysis in poultry-related social media texts by integrating domain-specific lexicons and contextual modeling, outperforming existing models.
Contribution
The paper introduces PoultryLeX-Net, a domain-adaptive dual-stream transformer that combines lexicon-guided and contextual streams for improved poultry sentiment analysis.
Findings
Achieved 97.35% accuracy in sentiment classification.
Outperformed baseline models like DistilBERT and RoBERTa.
Provided interpretable thematic insights via LDA.
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…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Digital Marketing and Social Media · Food Supply Chain Traceability
