New Web 2.0-based technologies have emerged in the field of competitor/marketintelligence. This paper discusses the factors influencing long-term product development,namely coal combustion long-term R&D/Carbon Capture and Storage (CCS) technology, andpresents a new method application for studying it via opinion mining. The technology marketdeployment has been challenged by public acceptance. The media images/opinions of coal powerand CCS are studied through the opinion mining approach with a global machine learning basedmedia analysis using M-Adaptive software. This is a big data-based learning machine mediasentiment analysis focusing on both editorial and social media, including both structured datafrom payable sources and unstructured data from social media. If the public acceptance isignored, it can at its worst cause delayed or abandoned market deployment of long-term energyproduction 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 researchproblem. Secondly, from the case study, it is apparent that CCS is unknown also based on thistype of approach. Finally, poor media exposure may have influenced technology marketdeployment in the case of CCS.
{"title":"Measuring public acceptance with opinion mining: The case of the energy industry with long-term coal R&D investment projects","authors":"K. Nuortimo","doi":"10.37380/JISIB.V8I2.319","DOIUrl":"https://doi.org/10.37380/JISIB.V8I2.319","url":null,"abstract":"New Web 2.0-based technologies have emerged in the field of competitor/marketintelligence. This paper discusses the factors influencing long-term product development,namely coal combustion long-term R&D/Carbon Capture and Storage (CCS) technology, andpresents a new method application for studying it via opinion mining. The technology marketdeployment has been challenged by public acceptance. The media images/opinions of coal powerand CCS are studied through the opinion mining approach with a global machine learning basedmedia analysis using M-Adaptive software. This is a big data-based learning machine mediasentiment analysis focusing on both editorial and social media, including both structured datafrom payable sources and unstructured data from social media. If the public acceptance isignored, it can at its worst cause delayed or abandoned market deployment of long-term energyproduction 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 researchproblem. Secondly, from the case study, it is apparent that CCS is unknown also based on thistype of approach. Finally, poor media exposure may have influenced technology marketdeployment in the case of CCS.","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45558960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：