Large-scale specialized electromechanical manufacturing faces increasing challenges arising from small-batch orders, process diversification, and uncertainty in downstream market conditions. Under such circumstances, timely identification of external market signals is important for supporting planning-related decisions in intelligent manufacturing. This study proposes a text-driven market sentiment analytics framework that leverages multi-source textual data to extract decision-relevant sentiment signals. A hybrid framework, termed LBBC, is developed by integrating topic modeling, contextual semantic representation, sequential learning, and structured prediction to capture the temporal dynamics of market sentiment. Using engineering-type special-purpose vehicles as a representative case, the framework analyzes sentiment information derived from news reports, industry analyses, and expert commentaries. Experimental results show that the LBBC framework consistently outperforms several baseline text-based models in sentiment classification tasks. To further consider the practical relevance of the extracted sentiment signals, aggregated monthly sentiment scores are discussed in relation to downstream market activity indicators associated with engineering-type special-purpose vehicles. The analysis suggests that the sentiment series may provide useful contextual signals for interpreting external market conditions and supporting planning-related decisions under uncertainty. Overall, the proposed framework is intended as a decision-support tool rather than a standalone demand forecasting model, and it provides a practical basis for incorporating text-based market intelligence into intelligent manufacturing systems.
Zhang et al. (2026) studied this question.