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.

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 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.

Throughout history, the Black Diaspora has used fashion as a form of protest. The element of fashion is often overlooked when considering the history and struggle for Black equality, because it is less tangible or definable in terms of its influence and effect, but it is still important because Black males resist the dominant culture via dress by dressing in military uniforms, creating their own style, and using different colors in their dress. Studying the Black struggle in American history during specific periods is one way to better understand opposition to the majority culture through fashion. We should also consider the mood of a social system when examining the dress of a particular group during conflicts. Hence, the purpose of this study is to investigate the role of fashion as a protest tool against the majority culture, and the social mood that affects the fashion choices of Black males. The study focuses on Black fashion from 1910 to 2015. Text data were collected and analyzed from articles published in The Crisis magazine, and men's fashion was specially examined. Additionally, images were studied via visual ethnography and images were coded based on color choice, fit, and accessories. For conducting sentiment analysis, lexicons were used, and the text was examined for negative sentiment. The overall negative sentiment of the document was obtained. Graphical analyses are included to present the findings. The findings, conclusions, limitations, and future research are discussed.

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Depression and bipolar disorder are two mental disorders which left untreated can have a devastating effect on a persons life as they are considered both chronic and disabling. Seeking help is often a big step that can be procrastinated for years, and misdiagnosis is a very common problem once contact with psychiatric care has finally been established. This paper investigates the correlation between posting patterns on Twitter and suffering from these diagnoses. For each day of the past year we quantify cues for emotional intensity and polarity, involvement with their social network and activity as well as metrics previously shown to be associated with depression. A number of statistical tests, including Anova, t-testing and Covariance analysis, are then constructed and fitted over our data. Our results show a significant difference between our groups in affective language use tied to emotional polarity as well as an elevated use of first person personal pronouns for both the depressed and bipolar group. These findings indicate strongly that our approach is valid for finding cues about mental illness, however the strong limitations in our data collections approach needs to be addressed in order for our results to have real scientific merit. This study is motivated by the need for finding predictive models for mental disorders, and to better understand the disorders themselves. Predictive models can be helpful for proper diagnosis by a clinical psychologist as well as for helping more people seek treatment. / Depression och bipolär sjukdom är två psykiska sjukdomar som obehandlade kan ha en förödande effekt på en persons liv eftersom de anses både kroniska och förlamande. Att söka hjälp är ofta ett väldigt stort steg som kan prokrastineras i flera år, och dessutom är feldiagnosticering ett väldigt stort problem när en kontakt med psykiatrin väl har upprättats. Denna rapport undersöker korrelationen mellan inläggsmönster på Twitter och dessa två diagnoser. Vi kvantifierar varje enskild dag av det senaste året i termer av kännetäcken för en människas emotionella intensitet och polaritet, engagemang med sitt sociala nätverk och aktivitet, såväl som parametrar som i tidigare forskning visat sig associerade med depression. Vi använder sedan statistiska modeller såsom variansanalys, t-test och kovariansanalys över vår data. Våra resultat visar på en signifikant skillnad mellan våra grupper i affekterat språkbruk och hur det kopplas till emotionell polaritet. Vi visar även på ökat användande av pronomen i första person singular hos våra bipolära och deprimerade grupper. Dessa resultat tyder på att vår metod är giltig för att hitta indikationer för mental ohälsa, men begränsningar i vår datainsamling behöver adresseras innan våra resultat kan ha riktig vetenskaplig betydelse. Den här studien är motiverad av behovet av att finna modeller med prediktiv kraft för psykisk ohälsa, och att bättre förstå depression och bipolar sjukdom som helhet. Prediktiva modeller kan vara hjälpsamma för korrekt diagnossättning av en klinisk psykolog samt att hjälpa individer att söka behandling.

Attitydanalys är ett delfält av språkteknologi (NLP) som försöker analysera känslan av skriven text. Detta är ett komplext problem som medför många utmaningar. Av denna anledning har det studerats i stor utsträckning. Under de senaste åren har traditionella maskininlärningsalgoritmer eller handgjord metodik använts och givit utmärkta resultat. Men den senaste renässansen för djupinlärning har växlat om intresse till end to end deep learning-modeller.Å ena sidan resulterar detta i mer kraftfulla modeller men å andra sidansaknas klart matematiskt resonemang eller intuition för dessa modeller. På grund av detta görs ett försök i denna avhandling med att kasta ljus på nyligen föreslagna deep learning-arkitekturer för attitydklassificering. En studie av deras olika skillnader utförs och ger empiriska resultat för hur ändringar i strukturen eller kapacitet hos modellen kan påverka exaktheten och sättet den representerar och ''förstår'' meningarna. / Sentiment analysis is a subfield of natural language processing (NLP) that attempts to analyze the sentiment of written text.It is is a complex problem that entails different challenges. For this reason, it has been studied extensively. In the past years traditional machine learning algorithms or handcrafted methodologies used to provide state of the art results. However, the recent deep learning renaissance shifted interest towards end to end deep learning models. On the one hand this resulted into more powerful models but on the other hand clear mathematical reasoning or intuition behind distinct models is still lacking. As a result, in this thesis, an attempt to shed some light on recently proposed deep learning architectures for sentiment classification is made.A study of their differences is performed as well as provide empirical results on how changes in the structure or capacity of a model can affect its accuracy and the way it represents and ''comprehends'' sentences.