New Web 2.0-based technologies have emerged in the field of competitor/market intelligence. This paper discusses the factors influencing long-term product development, namely coal combustion long-term R&D/Carbon Capture and Storage (CCS) technology, and presents a new method application for studying it via opinion mining. The technology market deployment has been challenged by public acceptance. The media images/opinions of coal power and CCS are studied through the opinion mining approach with a global machine learning based media analysis using M-Adaptive software. This is a big data-based learning machine media sentiment analysis focusing on both editorial and social media, including both structured data from payable sources and unstructured data from social media. If the public acceptance is ignored, it can at its worst cause delayed or abandoned market deployment of long-term energy production technologies, accompanied by techno-economic issues. The results are threefold: firstly, it is suggested that this type of methodology can be applied to this type of research problem. Secondly, from the case study, it is apparent that CCS is unknown also based on this type of approach. Finally, poor media exposure may have influenced technology market deployment in the case of CCS.

中文翻译：

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通过意见挖掘来衡量公众的接受度：以长期煤炭研发投资项目为例的能源行业

新的基于Web 2.0的技术已经出现在竞争对手/市场情报领域。本文探讨了影响长期产品开发的因素，即煤燃烧的长期研发/碳捕集与封存（CCS）技术，并提出了一种通过观点挖掘进行研究的新方法应用。技术市场的部署受到公众接受的挑战。通过意见挖掘方法，使用基于M-Adaptive软件的基于全局机器学习的媒体分析，研究了煤电和CCS的媒体图像/观点。这是一个基于大数据的学习机媒体情绪分析，同时关注编辑媒体和社交媒体，包括来自付费来源的结构化数据和来自社交媒体的非结构化数据。如果公众接受度被忽略，在最坏的情况下，它会导致长期能源生产技术的市场部署延迟或放弃，并伴有技术经济问题。结果有三个方面：首先，建议将这种方法论应用于此类研究问题。其次，从案例研究中可以明显看出，基于这种方法，CCS是未知的。最后，在CCS的情况下，不良的媒体曝光可能会影响技术市场的部署。