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The competition inside higher education institutions, namely universities, is tightening, putting emphasize on competitive intelligence (CI) function. At the same time, communication has shifted to digital channels, this trend was largely influenced by Corona virus pandemic. This presents a challenge for university reputation measurement and ranking, while the electronic word to mouth (E-wom) is more challenging to measure, control or influence than the issues measured in traditional university rankings. While traditional metrics are based on measuring academic reputation via surveys and gathering data from research organisations, this paper presents a way to include AI, namely chatGPT and big-data based media-analytics with social media sentiment to aid analysing the reputation of a University. Results based on Finnish universities indicate, that differences between media visibility and sentiment exist, and can be to some extent utilized in rating universities in local level and also generalize to global level, finally targeting to URS (University reputation score) -index. Due to complexity of measuring the reputation of the university strictly via AI and automated opinion mining, several limitations exist. The context of Finnish universities were chosen in order to limit the scope of the analysis. [ABSTRACT FROM AUTHOR]

The opposition to a deployed technology in large construction projects can grow step by step when transferred from a global level to local project delivery. Large construction projects with specific technology implementations put pressure on local public acceptance and community involvement. This pressure is transferred to project management, how to deal with the issue of stakeholder acceptance before, during, and after project execution. Hence, understanding public acceptance and project-specific reputation can prove beneficial. Utilized mostly in the company Market Intelligence function(MI), modern large dataset media analytics enables mining technology-related sentiments on global, regional, or local project levels. This paper measures the media sentiment towards three large Finnish construction projects. The specific interest is to investigate which stakeholder groups are visible through the editorial and social media and how these can be classified according to the level of required information or participation level. The aim is to gain a numerical value for project reputation, a concept belonging to the marketing field of studies. Relevant technology deployment indications are provided, and a stairs of acceptance concept is conceptualized to reflect the project-specific public acceptance. Specific needs to increase efforts at a local project level are indicated. The means to counteract local resistance can involve the mode of project execution or social marketing. The new algorithm-based method for measuring public acceptance and the introduced stairs of acceptance concept may bring project-level benefits by providing the added focus for increasing public acceptance. [ABSTRACT FROM AUTHOR]

This study aims to analyse solar power acceptance by different methods in various knowledge domains to gain a holistic view of global, regional, and local acceptance. This includes considering different related aspects of solar energy, including the overall concept, solar panel, the device converting sunlight into electricity, and photovoltaics, the technology. This multidisciplinary approach is possible through the advancement of artificial intelligence technology. Technology acceptance and sentiment, the emotion, are different concepts with slightly different influences on technology deployment. Acceptance can be granted as a social license and can be affected by how the media discusses the technologies. The acceptance further influences investment decisions and wider technology adoption. Sentiment can be obtained by machine or human-made analysis, in which the polarity (positive, negative, or neutral) is defined while the acceptance levels are indicative. This study applies opinion mining, chat generative pre-trained transformer, and generalised aggregated lexical tables methods to analyse the acceptance and sentiment of solar power at different levels. The findings and the original contribution involve highlighting the potential of artificial intelligence to study general acceptance. Artificial intelligence appears capable of providing a fast indication of both media sentiment and the level of acceptance of solar power. Traditional opinion mining seems to be more capable of acknowledging trends. This supports understanding the market environment and factors affecting technology development and deployment. Decision-making can benefit from a fast indication. [Display omitted] • Assessing global, regional, and local acceptance of solar power. • Combining opinion mining, ChatGPT, and CA-GALT to analyse general acceptance. • AI provides a fast indication of both media sentiment and the level of acceptance. • Understanding energy technology acceptance-related chains of reasonings. • Techno-economic context to complement views with causal acceptance models. [ABSTRACT FROM AUTHOR]

A social media (SoMe) firestorm can present a liability for personal brands via the loss of reputation, as well as for the organisational brand image. The drastic measures often taken in these situations, especially in cases of negative media attention or a scandal, usually involve dismissal of the related persons. Hence, predicting, monitoring, analysing and measuring SoMe firestorms related to organisations or individuals can be beneficial. This paper describes SoMe firestorms and their effect, using media analysis involving opinion mining. The analysis focuses on the human trash (ihmisroska) scandal that was caused by a local centre party politician in Finland. The politician caused a SoMe firestorm by describing homeless people and substance addicts as 'human trash'. The analysis utilises machine learning to classify 3300 media hits in the Finnish language to analyse their sentiment during the SoMe firestorm. General conclusions are drawn about the spread and influence of the SoMe firestorm to form a basis for wider global generalisation. The study formulates a scale for quantifying and analysing the influence of SoMe firestorms. The scale includes three classes relating to the exponential rise of the effect, starting from 1, with 3 being the highest. This scale aligns with the literature, which states that these events usually follow the same pattern. The case example provides further direction for the presented 1–3 level scale. [ABSTRACT FROM AUTHOR]

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 are 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. [ABSTRACT FROM AUTHOR]

The challenge in today's corporations is that even though the technology portfolio of a company plays a crucial role in delivering revenue--falling as a topic mainly under the area of technology management--technology may have a negative image due to observed risks or failing the sustainability criteria. It may influence the company's image and brand image, possibly also influencing decisions at corporate level. The monitoring of technology sentiments is therefore emphasized, benefiting from the advanced methods for business environment scanning, namely market and competitor intelligence functions. This paper utilizes a new big data based method, mostly utilized in market(MI)/competitor intelligence(CI) functions of the company, opinion mining, to analyse the global media sentiment of nuclear power and projects deploying the technology. With this approach, it is easier to understand the linkage to corporate images of companies deploying the technology and also related corporate decisions, mainly done in the areas of technology market deployment, marketing and strategic planning. The results indicate how the media sentiment towards nuclear power has been mostly negative globally, particularly in social media. In addition, results from similar analyses from a single company's images for the companies currently deploying the technology are seemingly less negative, indicating the influence of company's communication and branding activities. This paper has implications showing that a technology's media sentiment can influence a company's brand image, marketing communications and the need for actions when technology is deployed. In conclusion, there seems to be a need for better co-operation between different corporate functions, namely technology management, MI, marketing and strategic planning, in order to indicate technology image impacts and also counteract firestorms from social media. [ABSTRACT FROM AUTHOR]

Abstract 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 media image a factor for technology market deployment, similar to technical, legal, and economic factors. Public acceptance tends 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 are analysed by using the M-Adaptive tool for media monitoring to obtain the media sentiment. The analysis is rather broad, including three million social media platforms and various news outlets in many regions and 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 public sentiment towards power production technologies varies among different technologies, and between editorial publications and social media. Editorial content is usually constructed by using news frames, whereas social media includes more emotional content from single users. A potential chain from media image to energy technology market deployment is suggested. The findings support the notion of social media having an increasing role in shaping public opinion, which may need to be acknowledged to a larger extent. Highlights • This paper studies the energy technology media image effect to commercialisation. • This is studied in the case of power technologies. • The method used is opinion mining based on artificial learning machine based media-analysis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]