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.

Opinion Target Extraction (OTE) is a well-established subtask of sentiment analysis. While detecting sentiment polarity is useful in itself, the ability to extract the targets of the opinions allows for more thorough decision making. For example, an owner of a restaurant needs to know whether the guests are complaining about the food, or the ambience, or any other aspect of their establishment, etc. Despite the lexical information being crucial for the task, syntactic structures have potential in being used to correctly decide among multiple candidate entities. Rules based on such structures have been used previously for the task. The objective of this thesis is to investigate, whether syntactic information influences the behavior of the state-of-the-art models such as recurrent neural networks for the OTE task. We did not find any substantial evidence to suggest that adding the syntactic information influences the behavior of the models.

As the world is gravitating toward an information economy, it has become more and more critical for an investor to understand the impact of data and information. One of the sources of data that can be converted into information are texts from microblogging platforms, such as Twitter. The user of such a microblogging account can filtrate opinion and information to millions of people. Depending on the account holder, the opinion or information originated from the designated account may lead to different societal impact. The microblogging scope of this investigation are politicians holding a Twitter account. This investigation will look into the relationship between political tweets' sentiment and market movement and the subsequent longevity of such an effect. The classified sentiments are positive or negative. The presence of artificial intelligence is vital for a data-driven investigation; in the context of this investigation, artificial intelligence will be used to classify the sentiment of the political tweet. The methods chose to assess the impact of a political tweet and market movement is event-study. The impact is expressed in either a positive or a negative cumulative abnormal return subsequent to the political tweet. The findings of the investigation indicate that on average, there is no statistical evidence that a political tweets' sentiment leads to an abnormal return. However, in specific cases, political tweet leads to abnormal return. Moreover, it has been determined that the longevity of the effect is rather short. This is an interdisciplinary approach that can be applied by individual and institutional investors and financial institutions.

This paper aims to show how User Generated Content can create value for Smart Tourism Destinations. Applying the analysis on 5 different cases in the region of Stockholm to derive patterns and opportunities of value creation generated by UGC in tourism. Findings of this paper is also discussed in terms of improving decision making, possibilities of new business models and importance of technological improvements on STD’s. Finally, thoughts on models are presented for researchers and practitioners that might be interested in exploitation of UGC in the context of information-intensive industries and mainly in Tourism.

Environmental, Social and Governance (ESG) integration in financial activities is a crucial topic that is gaining importance in financial markets. During the years, many studies have been conducted about Initial Public Offering (IPO) and underpricing since they are fundamental aspects of firms’ lifecycle. Nevertheless, none of these studies have appropriately related firms’ ESG characteristics to IPO underpricing. In order to fill this knowledge gap, this thesis’s purpose is to investigate whether the ESG factors of a firm have effects on its IPO underpricing in the U.S stock market. The U.S has been chosen as it is the biggest stock market in the world and because of the quality and reliability of the data available for this country.  A quantitative study is applied to investigate the relationship between ESG characteristics of the firms and the level of underpricing. First, to obtain the measurement of the ESG level of the pre-IPO firms, we have conducted two textual analysis of IPO prospectus, namely, term frequency and sentiment analysis. These indicators aim to show the disclosure level of ESG factors and whenever ESG is perceived negatively or positively by the market. Successively, the multiple regression is performed for each ESG indicator to find which measures have the analytical abilities to explain IPO underpricing. Based on the multiple regression results, we can conclude that the frequency of environmental & governance terms occurred in IPO prospectus, the negative tone, and the overall sentiment of the environmental context are significantly explaining IPO underpricing. These results have given meaningful answers for our research. The market does not perceive the social factors of a firm in the IPO context. On the other hand, environmental and governance aspects still attract the market’s attention in different ways. The market is concerned about the disclosure level of the governance activities and whether these activities are sufficiently mentioned in the prospectus. Meanwhile, the market takes into serious consideration the environmental activities of a firm by assessing the qualities of these activities. Moreover, the market is more sensitive to the negative information about environmental content than positive information in the IPO context. The textual analysis methods applied in this thesis have some limitations. However, this study has the reliability to confirm that some companies’ ESG factors affect IPO underpricing. As a consequence, it is possible to state that the market cares about  ESG issues.