Hybrid approach in digital humanities research:a global comparative opinion mining media study

Abstract Digital humanities is a strongly emerging research field including various algorithm and big-data based methods, suitable for various research topics. In this dissertation, an opinion mining approach was taken to discover the global media sentiment toward renewable and nuclear power, in order to test and compare the algorithm based approach utilizing a large dataset to traditional ones. This thesis analyzes the media sentiment toward different near-zero emission power production technologies in order to describe differences in different technologies. The method used is opinion mining: big-data-based machine learning enhanced media analysis made by using M-Adaptive tool for media monitoring. As a summary, sentiment towards different global power production forms has been identified in this study, complemented by more qualitative human analysis in the case of nuclear power. The differences are highlighted and also compared against literature from communication, digitalization, social media influence and opinion mining, an area belonging to the multidisciplinary field of computational linguistics. A principal finding is, that an opinion mining approach can be used to reveal the global media sentiment of different energy technologies, both in editorial publications and social media. The media sentiment in social media provides a more unfiltered point of comparison to support that of edited media, and represents one possibility to discover opposition groups communicating via SoMe. Editorial media analysis can provide information from content created mostly in editorial style, with news frames. The media analysis reveals solar and wind power being the best-known technologies with positive media sentiment. Biomass power appears less known, yet with an inclination towards positive sentiment. Hydropower is distinctly present in editorial media with a bit lower inclination towards positive sentiment, and nuclear has clearly negative global SoMe sentiment with differences on the local level, especially in editorial sentiment. This indicates a possible presence of favourable attiture from Finnish press and corporate/PR -communication activities, further studied via qualitative manual rhetoric analysis. Ultimately, a hybrid research approach for studying this type of topics is presented.Tiivistelmä Digihumanismi on voimakkaasti yleistyvä humanistisen tutkimuksen ala, jossa hyödynnetään digitaalisia menetelmiä, kuten algoritmejä ja tiedonlouhintaa suuresta massasta (ns. big-data). Tässä väitöskirjassa analysoidaan mielipidelouhinnalla maailmanlaajuinen uusiutuvien energiatuotantomuotojen mediasentimentti, verrataan sitä ydinvoimaan ja menetelmää perinteisiin sisällönanalyysimenetelmiin. Tutkimuksessa hyödynnetään M-Adaptive ohjelmistoa. Tuloksena on saatu globaali mediahuomiojakauma vuoden ajalta eri sähkön tuotantomuodoille, jota on täydennetty ihmisten tekemällä analyysillä ydinvoiman tapauksessa. Selkeät erot eri tuotantomuotojen mediahuomion määrässä ja sentimentissä täsmäävät sekä toisiinsa että viestinnän kirjallisuuteen. Yksi päätutkimustuloksista on, että mielipidelouhintaa voidaan käyttää mittaamaan eri energiatuotantomuotojen globaalia mediahuomiota ja sentimenttiä sekä toimitetussa sisällössä, että sosiaalisessa mediassa. Sosiaalinen media on sisällöltään suodattamattomampaa kuin toimitettu sisältö, ja voi sisältää enemmän mm. aktivistiryhmien kommentointia. Toimitettu sisältö on tehty ns. journalistisella rakenteella, jonka pitäisi olla sentimenttineutraalimpaa kuin sosiaalinen media, ja tämä näkyy selvästi myös tutkimuksen aineistoissa. Media-analyysin perusteella aurinko- ja tuulivoima ovat tunnetuimpia uusiutuvia energiatuotantotekniikoita, joilla on myös positiivinen sentimentti. Uusiutuva energiatuotanto biomassasta on vähemmän tunnettu, mutta sentimentti on positiivinen. Vesivoima on ollut paljon esillä toimitetussa sisällössä ja sosiaalisessa mediassa positiivisella sentimentillä. Vaikka ydinvoiman sentimentti on selkeästi negatiivinen sosiaalisessa mediassa, paikallistason projekteissa on selkeitä eroja etenkin toimitetussa sisällössä. Yksi mahdollinen selitys tälle ovat paikalliset tiedotustoimet, lehdistön myönteinen suhtautuminen sekä yritysviestintä, jota tutkitaan retoriikka-analyysissä yhdestä esitteestä. Tämä täydentää väitöskirjan hybridi-tutkimusotteen

Measuring public acceptance with opinion mining:the case of the energy industry with long-term coal R&D investment projects

Abstract 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

Public acceptance in energy technology development and deployment:opinion mining case study

Abstract The motivation for this dissertation stems from the energy industry product development, where the solid fuel combustion technology development can take a long-time. During the long development time, the external market conditions behind the initial investment decisions may change. The external market environment includes factors such as CO2-price in European emission trading system, environmental regulations, and also public acceptance influencing political decision-making, and regulatory environment development. The negative change in these factors can cause delayed or abandoned product market deployment in the end of product development cycle. The objective of this dissertation is to clarify the factors that affect technology management and deployment in the energy industry, and to gain understanding how digital market environment monitoring tools, namely opinion mining could be used to support technology strategy process. The specific interest is on bigdata analysis, and the acceptance of energy technologies. The principal results indicate that constant market environment monitoring can be utilised to see change in factors behind the investment decisions. This may enable making alterations to the chosen product development path within the existing realities. The carbon capture and storage specific case indicates that public acceptance of technology is one of the important factors. The factors influencing technology market deployment were mainly external market factors, such as CO2-price, lack of regulations and public acceptance, including perceived stakeholder risk towards the CO2 end-storage. The created new understanding enables actors in the energy technology sector to better account for public acceptance in the technology development and deployment considerations.Tiivistelmä Tämä väitöskirja käsittelee energiateollisuuden tuotekehitystä, missä kiinteän polttoaineen polttotekniikan kehittäminen voi viedä pitkän aikaa, jopa kymmeniä vuosia. Pitkän tuotekehityksen aikana ulkoinen liiketoimintaympäristö, johon alkuperäinen investointipäätös on perustunut, on voinut muuttua. Ulkoinen liiketoimintaympäristö sisältää tekijöitä kuten hiilidioksidin hinta EUn päästökaupassa, ympäristölainsäädäntö ja teknologian yleinen hyväksyttävyys, joka vaikuttaa poliittiseen päätöksentekoon ja sitä kautta uuteen ympäristölainsäädäntöön. Negatiivinen muutos näissä tekijöissä voi johtaa viivästyneeseen tai täysin estyneeseen tuotteen kaupallistumiseen tuotekehityssyklin loppupäässä. Väitöskirjan tavoite on selvittää tuotekehitykseen vaikuttavia tekijöitä energia-alalla, ja tuottaa tietämystä siitä, miten digitaalisilla markkinaympäristön seurantatyökaluilla, etenkin mielipidelouhinnalla, voidaan tukea teknologiastrategiaprosessia. Erityinen fokusalue on bigdata-analytiikka ja energiateknologioiden yleinen hyväksyttävyys. Päätutkimustulokset liittyvät jatkuvaan liiketoimintaympäristön seurantaan, jota voidaan käyttää investointipäätösten taustalla olevien tekijöiden muuttumisen seurantaan. Tavoitteena on tehdä lähes reaaliaikaisia muutoksia valittuun tuotekehityspolkuun. Hiilidioksidin talteenoton ja varastoinnin tapauksessa erityisesti yleisen hyväksyttävyyden rooli korostui. Teknologian markkinoille tulon esteitä olivat etupäässä ulkoisen markkinaympäristön tekijät, kuten hiilidioksidin matala hinta, regulaatioiden puute ja yleinen hyväksyttävyys, jossa korostui varastointiin liittyvä sidosryhmäriski. Uusi tutkimustieto auttaa energiasektorin toimijoita ottamaan paremmin huomioon teknologian yleisen hyväksyttävyyden teknologian kehittämisessä ja markkinoille tulossa

Introducing concepts:stairs of acceptance and project specific reputation score. Exploring public acceptance in three Finnish construction projects via large dataset media-analytics

Abstract 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 andcommunity 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. Utilizedmostly 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

Improving strategic decision making with big data-based media analysis:the case of coal power

Abstract Big-data based methods are developed to aid corporate decision-making. This study utilises big-data based global media-analysis to clarify the role of coal-power related media-image in company decision-making. Opinion mining by a specific software tool for media-analysis and monitoring is utilised. The analysis bases on the notion that the media-image of company products — or specific technologies — may impact corporate investment and divestment decisions in the energy sector. The assumption is that coal-power related media-image may cause corporate brandimage pressures. The findings indicate that the general media-sentiment towards coal-power is negative, possibly influencing corporate decisions. The large negative media-sentiment towards coal-power may override the benefits of developing cleaner coal-power and related technologies such as carbon capture and storage and utilisation. The negative media-sentiment towards coal-power may mitigate the more positive image of related less-known technologies. Evidence is provided on the media-impact of coal-power related divestment decisions and potential impacts on decision making

Exploring new ways to utilise the market intelligence (MI) function in corporate decisions:case opinion mining of nuclear power

Abstract 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

Establishing an automated brand index based on opinion mining:analysis of printed and social media

Abstract 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

The first wave impact of COVID-19 pandemic on the Nasdaq Helsinki stock exchange:weak signal detection with managerial implications

Abstract The global pandemic caused by the coronavirus disease (COVID-19) came mostlyas a surprise and had a major effect on the global economy. This type of major events that canbring societies to nearly a total standstill are difficult to predict but have a significant impacton business activities. Nevertheless, weak signals might be possible to detect beforehand toenable preparation for the impact, both globally and locally. This study analyses the impact ofthe first wave of the COVID-19 pandemic on the Nasdaq Helsinki stock exchange by utilisinglarge-scale media analytics. This entails gaining data through media monitoring over the entireduration of the pandemic by applying black-box algorithms and advanced analytics on realcases. The data analysis is carried out to understand the impact of a such global event ingeneral, while aiming to learn from the potential weak signals to enable future marketintelligence to prepare for similar events. A social media firestorm scale, similar to the Richterscale for earthquakes or Sapphir-Simpson scale for hurricanes, is utilised to support theanalysis and assist in explaining the phenomenon. The results indicate that pandemics andtheir impact on markets can be studied as a subset of a media firestorms that produce a sharkfintype of pattern in analytics. The findings indicate that early signals from such events arepossible to detect by means of media monitoring, and that the stock exchange behaviour isaffected. The implications include highlighting the importance of weak signal detection fromabundant data to have the possibility to instigate preventive actions and prepare for such eventsto avoid maximum negative business impact. The early reaction to this type of events requiresa very streamlined connection between market intelligence and different business activities

Opinion mining approach to study media-image of energy production:implications to public acceptance and market deployment

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

Establishing social media firestorm scale via large dataset media analytics

Abstract 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