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In this work, the utilization of financial news alongside machine learning for predicting stock market movements is examined. The news are handled with various natural language processing methods for finding correlation between the derived attributes and stock market movements. The novelty of this work lies in the application of BNS and LDA methods as well as 2-word combinations alongside with LSTM neural network. The main point of the work is to examine the usefulness of the results achieved with the formerly mentioned methods and neural networks as well as comparing the results with market efficiency. In the research it was concluded that the models containing 2-word combinations derived with the BNS-method, produced differing results to those models, where the 2-word combinations were not used. However, the overall results followed random patterns and thus reliable results were not achieved. For achieving more reliable results, better approach could be predicting intraday stock market movements per Efficient Market hypothesis. Used datasets were possibly also too concise for the complexity of the problem. Tässä työssä tarkastellaan talousuutisten hyödyntämistä yhdessä koneoppimisen kanssa osakemarkkinoiden ennustamiseen. Uutisia käsitellään tietyillä luonnollisen kielen käsittelyn menetelmillä ja niistä pyritään löytämään korrelaatiota osakkeen liikkeiden kanssa. Työn uutuusarvona ovat BNS- ja LDA-menetelmien, sekä 2 sanan kombinaatioiden käyttö LSTM-neuroverkon yhteydessä. Työn pääasiallisena tavoitteena on tarkastella edellä mainittujen menetelmien ja neuroverkkojen yhdessä tuottamien tulosten hyödyllisyyttä ja niiden vertautumista markkinoiden tehokkuuteen. Tutkimuksessa selvisi, että mallit, jotka sisältävät BNS-menetelmällä johdettuja 2 sanan kombinaatioita, tuottavat poikkeavia tuloksia niihin malleihin verrattuna, joissa niitä ei käytetä. Tulokset kuitenkin noudattivat kaiken kaikkiaan satunnaista vaihtelua ja luotettavia tuloksia ei täten saatu. Tulosten parantamiseksi, parempi lähtökohta voisi olla päivän sisäisten vaihtelujen ennustaminen, markkinoiden tehokkuuden hypoteesin mukaisesti. Käytetyt tietoaineistot olivat myös mahdollisesti liian suppeat kompleksisuudeltaan vaativalle ongelmalle.

Fake news is not a novel concept but the scale of its spread and the damage it has and continues to cause is alarming. From the US presidential elections in 2016 to the COVID-19 pandemic and today, fake news has been circulating in news media corrupting the public opinion. Fake news alters democratic discussions polarizing people’s opinions sowing distrust in national institutions and setting different groups against each other. It is a threat to democracy and national security. It is crucial to prevent fake news from spreading and one solution is to create an automatic fake news detection system. A solution is researched using natural language processing (NLP) tasks, namely text classification. NLP is a type of artificial intelligence that is essentially taught to understand human language. Using thematic analysis, the main steps and techniques of fake news detection models are described and through a comparative analysis the state-of-the-art models are distinguished. And while there are many potential fake news detection models for English there is not much variety for other languages. So, it is additionally analysed if these benchmark models can be implemented for Finnish language as well. Valeuutiset eivät ole uusi käsite, mutta niiden leviämisen laajuus ja niiden aiheuttamat vahingot ovat huolestuttavia. Yhdysvaltain presidentinvaaleista vuonna 2016 COVID-19-pandemiaan ja nykypäivään asti, uutismediassa on kiertänyt valeuutisia, jotka muokkaavat yleisön mielipidettä. Valeuutiset muuttavat demokraattista keskustelua polarisoimalla ihmisten mielipiteitä kylväen epäluottamusta kansallisiin instituutioihin ja asettaen erilaisia ryhmiä toisiaan vastaan. Se on uhka demokratialle ja kansalliselle turvallisuudelle. On tärkeää estää valeuutisten leviäminen, ja yksi ratkaisu on luoda automaattinen valeuutisten havaitsemisjärjestelmä. Ratkaisua tutkitaan käyttämällä luonnollisen kielen käsittelyn (NLP) tehtäviä, etenkin tekstin luokittelua. NLP on tekoälyn tyyppi, missä tietokone opetetaan ymmärtämään ihmisten kieltä. Temaattisen analyysin avulla kuvataan valeuutisten havaitsemismallien päävaiheet sekä tekniikat, ja vertailevan analyysin avulla valikoidaan uusimmat ja onnistuneimmat mallit. Ja vaikka englannin kielellä on monia mahdollisia valeuutisten havaitsemismalleja, muille kielille ei ole paljon valikoimaa. Lisäksi analysoidaan, voidaanko nämä mallit toteuttaa myös suomen kielelle.

In today’s knowledge-based economies, it is generally accepted that innovations are integral to the foundation of both regional and national economic development, as well as one of the main causes for social and technical transitions. In an effort to boost and benchmark innovation, metrics and indicators have been designed to measure its various stages of development in order to gain insight into what is driving results. In an effort to make such measurements, a systems approach had been adopted in order to capture the dynamic and complex nature of innovation. However, an ecosystem approach has recently begun to attract attention as a framework for studying innovation. The term “innovation ecosystem” is often employed to explain a large and diverse set of participants and resources essential to the success of any innovation. Literature on innovation ecosystems emphasizes both the importance of a network of linkages between multiple actors and taking a holistic approach to include all players in the ecosystem. This is done to provide synergy, which has an effect on the overall outcome. This dissertation advances the existing research on innovation ecosystems by incorporating the soft aspects of innovation and studying social network services (SNSs) as a complementarity within said ecosystem. SNS platforms (e.g. Twitter, Facebook) provide opportunities for mass communication and interaction, both of which mediate societal discussion. These platforms create a unique opportunity to inform a holistic approach to innovation. The purpose of this thesis is to discuss the importance of SNSs in innovation ecosystems and attempt to operationalize the valuable data within SNSs for a deeper understanding of innovation. First, this thesis introduces the measurement and evaluation practices used, with particular effort made to highlight how the term “ecosystem” first emerged and then became associated with studies on innovation. To that end, an in-depth analysis of the innovation ecosystem research and citation network was conducted to assess the growing body of literature on this topic. Secondly, this study utilizes SNS data at both the microand the meso-level, meaning the company-, community-, and national-level, and provides novel insights. To do so, advanced textual analyses were performed and machine learning models were employed to explore the content of SNSs. These analysis resulted in several interesting findings regarding the role of content producer and content quality in the overall interaction within SNSs. This attempt to leverage SNSs for data was then furthered to include the design of a metric used to evaluate and establish benchmarks for counties based on entrepreneurial-oriented activity. For a more exploratory approach, SNSs data was analyzed to ascertain whether patterns existed within discussion topics and in proximity over time. Finally, the theoretical impact and methodological contributions to the literature on innovation ecosystems is included to show a novel approach to the use of SNS data. The findings should help scientists and practitioners to engage with SNSs in a more confident manner when an ecosystem-oriented approach is taken to evaluate innovation.

In 2006 UPM was able to gain a level of social legitimacy that allowed it to carry out one of the largest industrial restructuring programmes in Finnish industrial history, shut down major operations in Finland and still appear to be functioning in the interests of the nation as well as itself. This study considers and examines various contexts of this shutdown with the aim of demonstrating how profoundly mediated such organizational events are though they appear to be produced primarily through strategic company decisions. The study aims to examine the processes of mediation at two levels. At one level, through close analysis of press releases and newspaper reports in local and national newspapers, the study presents a discursive analysis of the Voikkaa case. The discursive analysis focuses on providing historical contexts for understanding why this organizational event was also an occasion for reimagining the past and future of the Finnish nation; spatial contexts for understanding the differing struggles over the meaning of the event nationally and regionally; and the temporal dynamics of the media reports. At another level, the study considers and refines methods for reading and analyzing mediation in organization studies. Bringing together recent research of media text–based legitimation studies, emerging research on organizational memory and organizational death and a Foucaultian analytics of power, this work suggests that organizational research needs to be less concerned with particular typologies and narratives of shutdowns, and more curious about the processes of mediation through which organizational events are imagined and remembered.

The increased amount of fake news has created a demand for different fake news detection methods. One way to detect fake news is with machine learning models. While there is a lot of research on different fake news detection models, there is not that much research done on the effects of sentiment information on the classification accuracy of the models. Sentiment information means the overall tone of each of the news articles in the dataset, whether it is positive, neutral, or negative. The goal of this thesis is to find out how sentiment information affects the performance of two of the most popular fake news detection machine learning models. These models are the Naïve Bayes and Support Vector Machine. The sentiment analysis was done with TextBlob and Vader, which are already tested and trained sentiment analysis tools available in Python. The results showed that sentiment information did not have any significant effect on the classification accuracy of the fake news detection models. In most of the cases the addition of sentiment information slightly decreased the accuracy of the models. Valeuutisten kasvanut määrä on aiheuttanut tarvetta löytää erilaisia menetelmiä valeuutisten havaitsemiseen. Yksi tapa havaita valeuutisia on koneoppimismenetelmien avulla. Vaikka erilaisia valeuutisten havaitsemiseen käytettyjä koneoppimismalleja on tutkittu paljon, ei ole vielä paljon tutkimusta siitä, miten tunnetieto vaikuttaa koneoppimismallien luokittelutarkkuuteen. Tunnetiedolla tarkoitetaan tietoaineiston kunkin uutisartikkelin yleistä sävyä, eli onko artikkeli positiivinen, neutraali vai negatiivinen. Tämän työn tavoitteena on selvittää, millainen vaikutus tunnetiedolla on kahteen suosituimpaan valeuutisten havaitsemiseen käytetyn koneoppimismallin suorituskykyyn. Nämä mallit ovat Naïve Bayes ja Support Vector Machine. Tunneanalyysi tehtiin käyttäen TextBlobia ja Vaderia, jotka ovat Pythonista löytyviä valmiiksi valmennettuja ja testattuja tunneanalyysityökaluja. Tulokset näyttivät, että tunnetiedolla ei ollut merkittävä vaikutus valeuutisia havaitsevien koneoppimismallien luokittelutarkkuuteen. Suurimmassa osassa tuloksista tunnetietojen lisääminen hiukan laski mallien tarkkuutta.

This doctoral study was conducted as an inquiry of the international entrepreneur in order to balance off the domination of firm-level studies and limited discussion of the individual as the initial driver in the international entrepreneurship phenomenon (IE). Two research questions guide the study: ‘How do individuals make sense of themselves as becoming and being international entrepreneurs?’ and ‘How to theorise of individuals becoming and being international entrepreneurs through a narrative approach?’. With a processual view of the phenomenon, this study embraces IE as a journey and approaches histories and sense-making of individuals through narrative inquiry, paying attention to the different efforts by which entrepreneurs (and researchers) contextualize—and constitute—the personal-level IE journeys. The qualitative dataset consists of interviews and historical data. Data analysis builds on ‘hermeneutic reasoning’, suggesting that meanings and implications of journeys individuals have undertaken can be better grasped after they have unfolded in time. The findings in the four publications construct the contribution of this article-based dissertation. Publications I, II and III embrace narrative sense-making as meaning structure to past actions and lived events and illuminate how the international entrepreneurial ‘self’ as an actor and agent in retrospect manifests individuals as the ‘autobiographical authors’ in regard to developmental, transitional and generational experiences and their meanings in becoming and being an international entrepreneur. They provide evidence of how the founders’ sense-making and identity work feed into the behavioural orientations and ‘bounded and boundaryless’ career journeys of becoming and being international. Publications I, III and IV are novel attempts to address empirically the social historic process in which IE is embedded and its significance for the individual. When analysed against the (inter)generational backdrop of individuals’ actions and life-events, we may trace how international entrepreneurs are the protagonists of their own generations and leaving a legacy to the next.

Recovery boilers are built all over the world. The roots of recovery technology are longer than the roots of recovery boilers. But it wasn’t until the invention of recovery boilers before the Second World War that the pulping technology was revolutionalized. This led to long development of essentially the same type of equipment, culminating into units that are largest biofuel boilers in the world. Early recovery technology concentrated on chemical recovery as chemicals cost money and if one could recycle these chemicals then the profitability of pulp manufacture would improve. For pulp mills the significance of electricity generation from the recovery boiler was for long secondary. The most important design criterion for the recovery boiler was a high availability. The electricity generation in recovery boiler process can be increased by elevated main steam pressure and temperature or by higher black liquor dry solids as well as improving its steam cycle. This has been done in the modern Scandinavian units. Post-print / final draft

The changes in the labor market accompanied by digitalization have influenced the approaches used for recruitment purposes. The development of machine learning and, specifically, natural language processing allowed new services for automated resume screening to appear. These services aim to reduce the time that HR specialists dedicate to sorting out unsuitable candidates, thus saving the costs per hire. This master thesis provides an analysis of the possible approach for matching CVs to JDs based on the taxonomy of competencies. In the literature review, there are the description and comparison of the different ontologies and taxonomies, and the analysis of the previous research performed in vacancy–CV matching provided. In the methodology part, there are the concepts of natural language processing and information extraction described, and the Sentence Transformer model and named entity recognition explained. Besides, evaluation approaches for taxonomy enrichment and vacancy–CV matching are suggested. In the solution development part, the described concepts and models are applied to the JDs and CVs data to perform taxonomy enrichment and matching tasks. The assessment of the matching algorithm performance based on the expert’s evaluation is presented. Based on the results obtained, the potential usage of the research and possible limitations are discussed.