Nuortimo, K. (2018) Measuring public acceptance with opinion mining: The case of the energy industry with long-term coal R&D investment projects. Journal of Intelligence Studies in Business. 8 (2) 6-22. Article URL: https://ojs.hh.se/index.php/JISIB/article/view/307
Measuring public acceptance with opinion mining : the case of the energy industry with long-term coal R&D investment projects
1Sumitomo SHI FW Energia Oy, P.O.Box 201, FIN-78201, Varkaus, Finland
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018110847684
|Publish Date:|| 2018-11-08
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.
This paper is the extended version of a paper from the ICI 2018 international conference on Competitive & Market intelligence, June 5–8 Bad Neuheim, Germany.
Journal of intelligence studies in business
|Pages:||6 - 22|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
222 Other engineering and technologies
© 2018 The Author. Attribution 3.0 Unported (CC BY 3.0)