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Shared micromobility defines as the shared use of light and low-speed vehicles such as bike and scooter in which users have short-term access on an as-needed basis. As shared micromobility, as one of the most viable and sustainable modes of transportation, has emerged in the U.S. over the last decade., understanding different aspects of these modes of transportation help decision-makers and stakeholders to have better insights into the problems related to these transportation options. Designing efficient and effective shared micromobility programs improves overall system performance, enhances accessibility, and is essential to increase ridership and benefit commuters. This dissertation aims to address three vital aspects of emerging shared micromobility transportation options with three essays that each contribute to the practice and literature of sustainable transportation. Chapter one of this dissertation investigates public opinion towards dockless bikes sharing using a mix of statistical and natural language processing methods. This study finds the underlying topics and the corresponding polarity in public discussion by analyzing tweets to give better insight into the emerging phenomenon across the U.S. Chapter two of this dissertation proposes a new framework for the micromobility network to improve accessibility and reduce operator costs. The framework focuses on highly centralized clubs (known as k-club) as virtual docking hubs. The study suggests an integer programming model and a heuristic approach as well as a cost-benefit analysis of the proposed model. Chapter three of this dissertation address the risk perception of bicycle and scooter riders’ risky behaviors. This study investigates twenty dangerous maneuvers and their corresponding frequency and severity from U.S. resident’s perspective. The resultant risk matrix and regression model provides a clear picture of the public risk perception associated with these two micromobility options. Overall, the research outcomes will provide decision-makers and stakeholders with scientific information, practical implications, and necessary tools that will enable them to offer better and sustainable micromobility services to their residents.

Cette thèse étudie les processus d’écriture sous une perspective métacognitive et métasubjective à partir de la théorie des opérateurs constructifs de Pascual-Leone (1976, 1984, 1987, 1989, 1995, 1997, 1998, 2000, 2001, 2012, 2013) et d’Efklides (2001, 2002, 2006, 2008, 2011, 2013). Il s’agit ici de comprendre quel pourrait être l’apport de cette théorie à l’écrit. Les chercheurs considèrent qu’à cause de sa capacité limitée la mémoire de travail soit responsable des difficultés à l’écrit (cf. Alamargot & Chanquoy, 2001 pour une revue complète de la littérature ; Olive, 2012). À partir d’un autre concept de base de la théorie des opérateurs constructifs, celui d’entraînement guidé (guided training, Pascual-Leone & Johnson, 2011), cette thèse suggère que l’intervention explicite de la métacognition – penser la pensée selon Flavell (1979) – sur le fonctionnement de l’attention mentale devrait contribuer à améliorer la performance en mémoire de travail et, par conséquent, la performance en production écrite. La question se pose alors de savoir : entraîner le développement des connaissances et des capacités métacognitives chez les enfants de l’école primaire pourrait-il contribuer à l’activation (inhibition) spontanée des connaissances (non) pertinentes et détourner ainsi le sentiment de difficulté des écoliers au profit de leur performance ? Les études expérimentales réalisées au sein de cette thèse se basent sur les trois systèmes du système métaconstructif de la théorie des opérateurs constructifs: le système d’apprentissage, le système attentionnel et le système personnel. Les recherches décrites dans la partie expérimentale ont été réalisées auprès d’enfants de CE2, CM1 et CM2. Les résultats montrent que les enfants sont en mesure de développer de nouvelles compétences métacognitives et qu’ils parviennent à déclencher des capacités appropriées à mieux gérer les ressources cognitives exigées par la tâche. / This dissertation studies writing under the metacognitive and metasubjective perspectives from Pascual-Leone’s Theory of Constructive Operators (1976, 1984, 1987, 1989, 1995, 1997, 1998, 2000, 2001, 2012, 2013) et d’Efklides (2001, 2002, 2006, 2008, 2011, 2013). Research on writing emphases the role working memory plays on the processes underlying it (cf. Alamargot & Chanquoy, 2001 for a review of the literature ; Olive, 2012), namely how the limited capacity of working memory affects children’s difficulties in writing. The notion of guided training (Pascual-Leone & Johnson, 2011) is here adopted suggesting the explicit implementation of metacognition, generally defined as thinking about thinking (Flavell, 1979), to manage mental attention resources while enhancing working memory performance, and as a consequence, written performance.This being said, the following questions apply: does training primary school children to develop metacognitive knowledge and abilities contribute to activate (inhibit) (non)pertinent knowledge while dispelling their feeling of difficult about writing?The experimental studies conducted within this research focus on the metaconsctructive system of the theory of constructive operators: the learning system, the attention system and the personal system. Results show that 3rd, 4th and 5th graders are able to develop new metacognitive knowledge about writing, furthermore, primary school children succeed in better handling the cognitive resources required by the task.

Twitter and other microblogging services are a valuable source for almost real-time marketing, public opinion and brand-related consumer information mining. As such, collection and analysis of user-generated natural language content is in the focus of research regarding automated sentiment analysis. The most successful approach in the field is supervised machine learning, where the three key problems are data cleaning and transformation, feature generation and model choice and training parameter selection. Papers in recent years thoroughly examined the field and there is a agreement that relatively simple techniques as bag-of-words transformation of text and a naive bayes models can generate acceptable results (between 75% and 85% percent F1-scores for an average dataset) and fine tuning can be really difficult and yields relatively small results. However, a few percent in performance even on a middle-size dataset can mean thousands of better classified documents, which can mean thousands of missed sales or angry customers in any business domain. Thus this work presents and demonstrates a framework for better tailored, fine-tuned models for analysing twitter data. The experiments show that Naive Bayes classifiers with domain specific stopword selection work the best (up to 88% F1-score), however the performance dramatically decreases if the data is unbalanced or the classes are not binary. Filtering stopwords is crucial to increase prediction performance; and the experiment shows that a stopword set should be domain-specific. The conclusion is that there is no one best way for model training and stopword selection in sentiment analysis. Thus the work suggests that there is space for using a comparison framework to fine-tune prediction models to a given problem: such a comparison framework should compare different training settings on the same dataset, so the best trained models can be found for a given real-life problem.

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This research is concerned with the identification of sentiment in multimodal content. This is of particular interest given the increasing presence of subjective multimodal content on the web and other sources, which contains a rich and vast source of people's opinions, feelings, and experiences. Despite the need for tools that can identify opinions in the presence of diverse modalities, most of current methods for sentiment analysis are designed for textual data only, and few attempts have been made to address this problem. The dissertation investigates techniques for augmenting linguistic representations with acoustic, visual, and physiological features. The potential benefits of using these modalities include linguistic disambiguation, visual grounding, and the integration of information about people's internal states. The main goal of this work is to build computational resources and tools that allow sentiment analysis to be applied to multimodal data. This thesis makes three important contributions. First, it shows that modalities such as audio, video, and physiological data can be successfully used to improve existing linguistic representations for sentiment analysis. We present a method that integrates linguistic features with features extracted from these modalities. Features are derived from verbal statements, audiovisual recordings, thermal recordings, and physiological sensors signals. The resulting multimodal sentiment analysis system is shown to significantly outperform the use of language alone. Using this system, we were able to predict the sentiment expressed in video reviews and also the sentiment experienced by viewers while exposed to emotionally loaded content. Second, the thesis provides evidence of the portability of the developed strategies to other affect recognition problems. We provided support for this by studying the deception detection problem. Third, this thesis contributes several multimodal datasets that will enable further research in sentiment and deception detection.

Des recherches en psychologie sociale ont montré qu’un sentiment de victimisation collective pouvait avoir des conséquences négatives sur les relations intergroupes. De plus, les membres de groupes en conflit peuvent également expérimenter de la « compétition victimaire », une rivalité au sujet de la gravité de leurs souffrances respectives. Cependant, ces recherches se sont principalement focalisées sur les relations qu’entretiennent des ennemis (passés ou actuels) ou d’anciens perpétrateurs avec leurs victimes. Dans cette thèse, nous soutenons que des groupes peuvent entrer en compétition au sujet de leur victimisation et ce, même s’ils ne peuvent être tenus responsables de leur victimisation respective. Dans de telles situations, la compétition porterait sur la reconnaissance de leur statut de victime plutôt que sur la sévérité de leurs souffrances respectives. A son tour, cette « compétition pour la reconnaissance du statut de victime » peut être associée à des attitudes intergroupes négatives. Afin de tester ces hypothèses, une analyse des discours de Dieudonné a été effectuée afin d’examiner un exemple sociétal dans lequel un membre d’une minorité connu pour sa lutte pour la reconnaissance de son endogroupe a exprimé des attitudes négatives vis-à-vis d’une autre minorité. Deux études corrélationnelles ont ensuite été menées en Belgique au sein de deux groupes minoritaires – les Africains sub-sahariens et les Musulmans – afin de tester un modèle basé sur des questions de reconnaissance. Trois réplications ont également été lancées en Pologne, Hongrie et Serbie afin de tester ce modèle auprès de groupes majoritaires. Ensuite, une étude expérimentale a été réalisée afin de s’assurer d’un lien causal entre le manque de reconnaissance du statut de victime et des attitudes intergroupes négatives. Enfin, une étude corrélationnelle a été menée en Pologne afin de tester le lien entre la compétition pour la reconnaissance du statut de victime et une prise de position contre la commémoration d’un exogroupe perçu comme plus reconnu que l’endogroupe. / Doctorat en Sciences psychologiques et de l'éducation / info:eu-repo/semantics/nonPublished

This paper aims to measure, compare and model Sovereign Risk. The risk position of South Africa compared to Emerging Markets as well as in comparison to Developed Markets is considered. Particular interest is taken in how the South African Sovereign Risk environment, and its associated determinants, differs and conforms to that of other Emerging Markets. This effectively highlights how the South African economy is similar to the Emerging Markets and where it behaves differently. Regression, optimisation techniques, dimension reduction techniques as well as Machine Learning techniques, through the use of sentiment analysis, is utilised in this research.

In today’s digital era, text data plays a ubiquitous role across various domains. This bachelor thesis focuses on the field of sentiment analysis, specifically addressing the task of classifying text into positive, negative, or neutral sentiments with the help of an AI tool. The key research questions addressed are: (1) How can an accurate sentiment classification system be developed to categorize customer reviews as positive, negative, or neutral? (2) How can the performance of the sentiment analysis tool be optimized and evaluated, considering the factors that influence its accuracy? (3) How does Chat-GPT evaluate text-based feedback from customers with our results as input, i.a. can"Artificial General Intelligence" be adapted to solve a specific problem in the domain of this work? To accomplish this, the study harnesses the power of RoBERTa, an implemented transformer model renowned for its prowess in natural language processing tasks. The model will mainly focus on review comments from Amazon and on the product, "Samsung Galaxy A53". A small comparative analysis will also be carried out with Chat-GPT and the RoBERTa model’s sentiment positions. The results demonstrate the effectiveness of the RoBERTa model in sentiment classification, showcasing its ability to categorize sentiments for different review comments. The evaluation process identified key factors that influence the tool’s performance and provided insights into areas for further improvement. In conclusion, this thesis contributes to the field of sentiment analysis by providing a comprehensive overview of the development, optimization, and evaluation of an AI-powered text analysis tool for the sentiment classification of customer reviews. The result affects the importance of understanding customer sentiment and providing practical implications for businesses to improve their decision-making processes and enhance customer satisfaction.

Customer reviews are important for both customers and companies. Customers want to find out if the product or service is what they need while companies want to figure out if their product is good enough for their customers. There is, however, an issue where customers very rarely write a product review. An example of a solution for this could be to let the customer choose between adjectives rather than write the entire review. To help future researchers find out if this could make customers more prone to write reviews, this study looks at the correlation between the sentiment and the rating, as well as the adjectives used when a rating and sentiment correlate. Other studies look at the correlation, or the precision of the tool used for sentiment analysis but do not go in-depth on what makes a review correlate with its rating. To study this, four datasets of reviews were used with a total of 105234 reviews. Then, using Stanford CoreNLP each review text got a predicted sentiment score. The Pearson coefficient was then used to find the correlation coefficient between ratings and sentiments. The conclusion is that there is a weak-moderate correlation between ratings and sentiment. Adjectives with a positive sentiment had a higher correlation than negative adjectives, however, most of them still had a low correlation. The sentiment correlates better when the reviews with only one sentence are omitted from the result.