Video games are experience-based products and user satisfaction is key for their popularity. To design for as strong an experience as possible, game developers incorporate evaluation methods that help to discover their users’ expectations and needs. Despite such efforts, problems still occur with the game design that lower the user experience. To counter these problems, the evaluation methods should be investigated and improved. To address this need, I have explored various design tools and user experience theories. Applying these in a game evaluation context, I have analyzed user-created game reviews and conducted longitudinal user interview- and game diary studies in connection to playing a newly released game, in other words different methods to take advantage of users' expectations, opinions, attitudes and experiences. One result of the analysis of the obtained data is a set of “slogans” that illustrate how and why users lose interest in a game. A second result is a method for extracting user attitudes from pre-produced user reviews and how this can be used in game development. Thirdly, I introduce an alternative model, aimed at game user experience development, the Playsona. The Playsona is a lightweight tool that introduces a variant of the Persona-method, specifically for video game design. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.</p>

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.

This research examines the confluence of consumers’ use of social media to share information with the ever-present need for innovative research that yields insight into consumers’ economic decisions. Social media networks have become ubiquitous in the new millennium. These networks, including, among others: Facebook, Twitter, Blog, and Reddit, are brimming with conversations on an expansive array of topics between people, private and public organizations, governments and global institutions. Preliminary findings from initial research confirms the existence of online conversations and posts related to matters of personal finance and consumers’ economic outlook. Meanwhile, the Consumer Confidence Index (CCI) continues to make headline news. The issue of consumer confidence (or sentiment) in anticipating future economic activity generates significant interest from major players in the news media industry, who scrutinize its every detail and report its implications for key players in the economy. Though the CCI originated in the United States in 1946, variants of the survey are now used to track and measure consumer confidence in nations worldwide. In light of the fact that the CCI is a quantified representation of consumer sentiments, it is possible that the level of confidence consumers have in the economy could be deduced by tracking the sentiments or opinions they express in social media posts. Systematic study of these posts could then be transformed into insights that could improve the accuracy of an index like the CCI. Herein lies the focus of the current research—to analyze the attributes of data from social media posts, in order to assess their capacity to generate insights that are novel and/or complementary to traditional CCI methods. The link between data gained from social media and the survey-based CCI is perhaps not an obvious one. But our research will use a data extraction tool called NetBase Insight Workbench to mine data from the social media networks and then apply natural language processing to analyze the social media content. Also, KH Coder software will be used to perform a set of statistical analyses on samples of social media posts to examine the co-occurrence and clustering of words. The findings will be used to expose the strengths and weaknesses of the data and to assess the validity and cohesion of the NetBase data extraction tool and its suitability for future research. In conclusion, our research findings support the analysis of opinions expressed in social media posts as a complement to traditional survey-based CCI approaches. Our findings also identified a key weakness with regards to the degree of ‘noisiness’ of the data. Although this could be attributed to the ‘modeling’ error of the data mining tool, there is room for improvement in the area of association—of discerning the context and intention of posts in online conversations.

This thesis deals with the field of Competitive Intelligence in relation to the possibilities of application of its methods and tools for competitive analysis of the market environment using modern virtual social networks. Theoretical part focuses on the characteristics of the market environment of ICT companies by using Porter's analysis and then it is focused on the description of selected tools and methods used to processing unstructured data and social networks analysis. The practical part is based on a real project which ran from early March 2013 at IBM Company. Practical part demonstrates current possibilities of information technology in the field of Competitive Intelligence.

Gender parity. Gender equality. Diversity and intersectionality. Are they understood to be one and the same thing? Whilst there is much public data and opinion on economic benefits to having gender parity within organisations and how it can help support women’s empowerment and inclusion in male dominated professional sectors, public perception on the topic may paint a different picture. In this thesis, the social media platform Twitter is used to collect data to conduct a content analysis in order to understand public sentiments in response to one company’s perceived success in their organisational gender parity initiative. That company is American tech organisation, Duolingo. In 2018, Duolingo posted via Twitter that they had achieved a 50:50 male:female ratio in their recruitment of new engineering hires. The response on Twitter reveals that whilst many Twitter users agreed with Duolingo that this was a success, many did not. The Tweets are classified and analysed according to sentiment and coded according to the core topic in their communication – gender parity, gender equality, and diversity and intersectionality - to gain an in-depth understanding into how the public understands and reacts to these concepts. By analysing 275 Tweets through textual and visual analysis, this thesis supports an investigation via case study as to whether or not gender parity is publicly perceived and understood as a positive organisational strategy towards gender equality. Or whether it is seen to be exacerbating gender inequalities and perpetuating gender and intersectional stereotyping, biases and norms.

Natural language processing (NLP) opens the possibilities for a computer to read, decipher, and interpret human languages to eventually use it in ways that enable yet further understanding of the interaction and communication between the human and the computer. When appropriate data is available, NLP makes it possible to determine not only the sentiment information of a text but also information about the author behind an online post. Previously conducted studies show aspects of NLP potentially going deeper into the subjective information, enabling author classification from text data. This thesis addresses the lack of demographic insights of online user data by studying language use in texts. It compares four popular yet diverse machine learning algorithms for gender and age segmentation. During the project, the age analysis was abandoned due to insufficient data. The online texts were analysed and quantified into 118 parameters based on linguistic differences. Using supervised learning, the researchers succeeded in correctly predicting the gender in 82% of the cases when analysing data from English online users. The training and test data may have some correlations, which is important to notice. Language is complex and, in this case, the more complex methods SVM and Neural networks were performing better than the less complex Naive Bayes and Logistic regression.

We used tweets to collect public discussion on organizations' activities during the specified Covid19 period. Through topic modeling, we were able to establish discussed topics in line with the organization's activities. Our research majored on tweets with matching hashtags W.H.O (world health organization) and coronavirus, covid19 or covid. We extracted five latent topics and explored the distribution or evolution of those topics over time. We were able to find people's opinions on hot topics (the period when a topic is mainly discussed); the hot topics reflect activities on the timeline of W.H.O during the specified period of the Pandemic. Our results show that the key topics are identified and characterized by specific events that happened during the specified period in our data. Our result describes the events that happened on the timeline of the W.H.O, showing the public opinion on each period a discussion is hot. It also shows how people's opinions revolve during the period. Our results will be helpful in identifying public sentiment on events, how people's opinion varies, and can also help understand different events of the organization based on the aim and objective of the event.

This work deals with the problem of text mining which is becoming more popular due to exponential growth of the data in electronic form. The work explores contemporary methods and their improvement using optimization methods, as well as the problem of text data understanding in general. The work addresses the problem in three ways: using traditional methods and their optimizations, using Big Data in train phase and abstraction through the minimization of language-dependent parts, and introduction of the new method based on the deep learning which is closer to how human reads and understands text data. The main aim of the dissertation was to propose a method for machine understanding of unstructured text data. The method was experimentally verified by classification of text data on 5 different languages – Czech, English, German, Spanish and Chinese. This demonstrates possible application to different languages families. Validation on the Yelp evaluation database achieve accuracy higher by 0.5% than current methods.

Sentiment visualization techniques are information visualization approaches that focus on representing the results of sentiment analysis and opinion mining methods. Sentiment visualization techniques have been becoming more and more popular in the past few years, as demonstrated by recent surveys. Many techniques exist, and a lot of researchers and practitioners design their own. But the question of usability of these various techniques still remains generally unsolved, as the existing research typically addresses individual design alternatives for a particular technique implementation only. Multiple surveys and evaluations exist that argue for the importance of investigating the usability of such techniques further. This work focuses on evaluating the effectiveness, and efficiency of common visual representations for low-level visualization tasks in the context of sentiment visualization. It shows what previous work has already been done by other researchers and discusses the current state of the art. It further describes a task-based user study for various tasks, carried out as an online survey and taking the task completion time and error rate into account for most questions. This study is used for evaluating sentiment visualization techniques on their usability with regard to several sentiment and emotion datasets. This study shows that each visual representation and visual variable has its own weaknesses and strengths with respect to different tasks, which can be used as guidelines for future work in this area.

De nos jours, les Réseaux Sociaux sont omniprésents dans tous les aspects de la vie. Une fonctionnalité fondamentale de ces réseaux est la connexion entre les utilisateurs. Ces derniers sont engagés progressivement à contribuer en ajoutant leurs propres contenus. Donc, les Réseaux Sociaux intègrent également les créations des utilisateurs ; ce qui incite à revisiter les méthodes de leur analyse. Ce domaine a conduit désormais à de nombreux travaux de recherche ces dernières années. L’un des problèmes principaux est la détection des communautés. Les travaux de recherche présentés dans ce mémoire se positionnent dans les thématiques de l’analyse sémantique des Réseaux Sociaux et de la génération des applications interactives personnalisées. Cette thèse propose une approche pour la détection des communautés d’intérêt dans les Réseaux Sociaux. Cette approche modélise les données sociales sous forme d’un profil utilisateur social représenté par un ontologie. Elle met en oeuvre une méthode pour l’Analyse des Sentiments basées sur les phénomènes de l’influence sociale et d’Homophilie. Les communautés détectées sont exploitées dans la génération d’applications interactives personnalisées. Cette génération est basée sur une approche de type MDA, indépendante du domaine d’application. De surcroît, cet ouvrage fait état d’une évaluation de nos propositions sur des données issues de Réseaux Sociaux réels. / Nowadays, Social Networks are ubiquitous in all aspects of life. A fundamental feature of these networks is the connection between users. These are gradually engaged to contribute by adding their own content. So Social Networks also integrate user creations ; which encourages researchers to revisit the methods of their analysis. This field has now led to a great deal of research in recent years. One of the main problems is the detection of communities. The research presented in this thesis is positioned in the themes of the semantic analysis of Social Networks and the generation of personalized interactive applications. This thesis proposes an approach for the detection of communities of interest in Social Networks. This approach models social data in the form of a social user profile represented by an ontology. It implements a method for the Sentiment Analysis based on the phenomena of social influence and homophily. The detected communities are exploited in the generation of personalized interactive applications. This generation is based on an approach of type MDA, independent of the application domain. In addition, this manuscript reports an evaluation of our proposals on data from Real Social Networks.