This paper analyses the media image of solar power in order to understand the recent technology development trends. The increase in both solar PV panels as well as concentrated solar power plants has been influenced by decrease in solar power price, as well as subsidies and general public acceptance. This paper focuses on the latter, through quantitative media analysis. This paper utilises a modern method for media sentiment analysis from both editorial and social media, learning machine based analysis including over 50 000 data points. The results indicate that sentiment toward solar power, especially in social media, has been mostly neutral or positive thus with expected positive effect on technology market deployment.

The nature of media image, through either traditional or social media may have an influence on public acceptance of energy technologies. The potential impact on decision-making can make the media image a factor for technology market deployment, similarly as technical, legal and economic factors. The public acceptance has a tendency to be shaped by how technologies are presented in the media. This study compares and analyses the media image of various power production technologies. Editorial and social media is analysed by using M-Adaptive tool for media monitoring to obtain the media sentiment. The analysis is rather covering by including three million social media platforms, and various news outlets in many regions, covering an enormous number of data points from which this study has selected over 250,000 for further analysis with the help of Artificial Intelligence. The results indicate that the public sentiment towards power production technologies varies among different technologies, and between editorial publications and the social media. The editorial content is usually constructed by using news frames, whereas social media includes more emotional content from single users. A potential reasoning from public image to energy technology market deployment is synthesised. The finding support the notion of social media having an increasing role, which may need to be acknowledged to a larger extent.

Public acceptance and positive media image are among the key features in technology market deployment aside any technical, legal and economic questions. The way technologies appear in various forms of media has a tendency to shape the public acceptance. This study analyses the media image of biomass power in order to understand the recent technology developments needed to overcome global warming. The media sentiment is analysed from both editorial and social media by using M-adaptive tool for media monitoring. The analysis covers three million social media platforms, hundred thousand news outlets in over seventy languages over 236 regions covering a vast number of data points. The results indicate that the public sentiment towards biomass power is more positive in editorial publications than in the social media. It appears that the increasing role of social media for public acceptance may need to be acknowledged in technology deployment issues.

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.

This article analyses the potential of using opinion mining based on big data to calculate a brand index to reflect brand image in the media. The study is realised as a combination of analysing previous literature and applying a media monitoring tool to analyse editorial publications and social media to gain brand-related media sentiment. The potential of opinion mining and the use of vast amounts of data is demonstrated. The results indicate that sentiment analysis based on big data has potential for automating the calculation of brand indices. It seems that big data can be used to compare brands and the nature of their media visibility. Marketing research and the analytics domain can benefit from big data and their related meaningful applications.