Psychological biases in the form of sentiment can affect various economic decisions including accounting choices. Broadly defined, the term sentiment refers to unjustified beliefs about the future cash flow prospects of the firm (Baker and Wurgler 2006). Asymmetric timely loss recognition (ATLR) is particularly prone to managerial sentiment because the decision to recognize economic gains and losses is based, in part, on managers’ beliefs about the likelihood of future economic events affecting the firms. In this study, I examine the effect of psychological biases about future performance on current accounting choices via the effect of market-level managerial sentiment on ATLR. I find that ATLR decreases with managerial sentiment and that periods of high managerial sentiment are associated with lower concurrent write-offs but higher subsequent write-offs. This study enhances the implications of sentiment on firms’ accounting choices by identifying a time-varying macroeconomic determinant of ATLR that is based on psychological biases about future performance.

Deep Learning models rely on a huge amount of labeled data to be created. However, there are a number of areas where labeling data is a costly process, making Deep Learning approaches unfeasible. One way to handle that situation is by using the Active Learning technique. Initially, it creates a model with the available labeled data. After that, it incrementally chooses new unlabeled data that will potentially increase the model accuracy, if added to the training data. To select which data will be labeled next, this technique requires a measurement of uncertainty from the model prediction, which is usually not computed for Deep Learning methods. A new approach has been proposed to measure uncertainty in those models, called Monte Carlo Dropout . This technique allowed Active Learning to be used together with Deep Learning for image classification. This research will evaluate if modeling uncertainty on Deep Learning models with Monte Carlo Dropout will make the use of Active Learning feasible for the task of sentiment analysis, an area with huge amount of data, but few of them labeled. / Modelos de Aprendizado Profundo necessitam de uma vasta quantidade de dados anotados para serem criados. Entretanto, existem muitas áreas onde obter dados anotados é uma tarefa custosa. Neste cenário, o uso de Aprendizado Profundo se torna bastante difícil. Uma maneira de lidar com essa situação é usando a técnica de Aprendizado Ativo. Inicialmente, essa técnica cria um modelo com os dados anotados disponíveis. Depois disso, ela incrementalmente escolhe dados não anotados que irão, potencialmente, melhorar à acurácia do modelo, se adicionados aos dados de treinamento. Para selecionar quais dados serão anotados, essa técnica necessita de uma medida de incerteza sobre as predições geradas pelo modelo. Entretanto, tal medida não é usualmente realizada em modelos de Aprendizado Profundo. Uma nova técnica foi proposta para lidar com a problemática de medir a incerteza desses modelos, chamada de Monte Carlo Dropout . Essa técnica permitiu o uso de Aprendizado Ativo junto com Aprendizado Profundo para tarefa de classificação de imagens. Essa pesquisa visa averiguar se ao modelarmos a incerteza em modelos de Aprendizado Profundo com a técnica de Monte Carlo Dropout , será possível usar a técnica de Aprendizado Ativo para tarefa de análise de sentimento, uma área com uma vasta quantidade de dados, mas poucos deles anotados.

The world in which we live has changed rapidly over the last few decades. Threats of bioterrorism, influenza pandemics, and emerging infectious diseases coupled with unprecedented population mobility led to the development of public health surveillance systems. These systems are useful in detecting and responding to infectious disease outbreaks but often operate with a considerable delay and fail to provide the necessary lead time for optimal public health response. In contrast, syndromic surveillance systems rely on clinical features (e.g., activities prompted by the onset of symptoms) that are discernible prior to diagnosis to warn of changes in disease activity. Although less precise, these systems can offer considerable lead time. Patient information may be acquired from multiple existing sources established for other purposes, including, for example, emergency department primary complaints, ambulance dispatch data, and over-the-counter medication sales. Unfortunately, these data are often expensive, sometimes difficult to obtain and almost always hard to integrate. Fortunately, the proliferation of online social networks makes much more information about our daily habits and lifestyles freely available and easily accessible on the web. Twitter, Facebook and FourSquare are only a few examples of the many websites where people voluntarily post updates on their daily behaviors, health status, and physical location. In this thesis we develop and apply methods to collect, filter and analyze the content of social media postings in order to make predictions. As a proof of concept we used Twitter data to predict public opinion in the form of the outcome of a popular television show. We then used the same methods to monitor and track public perception of influenza during the H1N1 epidemic, and even to predict disease burden in real time, which is a measurable advance over current public health practice. Finally, we used location specific social media data to model human travels and show how this data can improve our prediction of disease burden.

My work approaches the multitude of personal experience within the landscape, considering its cultural representation, aiming to give the environment agency within these tumultuous times. The following text is a personal narrative, realizing the many lenses through which a landscape can be experienced, including analyses of artists, writers, and musicians who have represented landscape through their own individuality.

Esta Tese tem como objetivo geral inserir a Análise de Sentimentos na gestão das Cidades Inteligentes, possibilitando a implementação de uma ferramenta que disponibilize informações que auxiliem na supervisão e gestão dessas cidades. Dentre os possíveis auxílios que podem ser prestados está a identificação de ações, meios de prevenção e predição de possíveis adversidades nos diversos Domínios de Interesse, além da busca por melhorias na qualidade vida da população, que pode ser feita por meio dessa análise, permitindo que os gestores dessas cidades possam tomar as melhores decisões de acordo com cada cenário. Este trabalho contribui com um novo método cujo o objetivo é o desenvolvimento de um Sistema de Análise de Sentimentos para Auxílio na Gestão das Cidades Inteligentes (ASCI). Esse Sistema é capaz de captar, tratar, processar, filtrar por Domínio de Interesse e avaliar os sentimentos contidos nas informações provenientes dos cidadãos de uma Cidade Inteligente. O método utiliza duas Fases de Mineração de Dados, uma para a classificação dos Domínios de Interesse e outra para a Análise de Sentimentos. Para o estudo de caso foi implementado o método ASCI por meio do qual são captadas informações provenientes da população de uma determinada região da cidade de São Paulo, por meio da Rede Social Twitter. Também foi realizado um estudo de classificação de sentimentos no Domínio específico do Transporte, no qual também foram utilizados, e tiveram seu desempenho avaliado, os classificadores do tipo Linear SVC, Logistic Regression, Multinomial Naive Bayes e Random Forest Classifier para identificar os sentimentos positivos, neutros e negativos dos tweets captados. Os dados foram avaliados usando duas técnicas de extração de características de texto: Bag of Words e TF-IDF. O método ASCI desenvolvido nesta Tese contribui de maneira relevante para a área de Análise de Sentimentos, uma vez que os resultados obtidos foram satisfatórios quando aplicado em cenários de Domínios de Interesse das Cidades Inteligentes. / The main objective of this work is to insert the Sentiment Analysis in the management of Smart Cities, enabling the implementation of a supervision and management tool in these cities. Among the possible aid services that can be applied, there is the identification of actions, ways of prevention and prediction of possible adversities in the various Domains of Interest, and also the search for improvements in the quality of life of the population. This can be done through this analysis, allowing the best decisions according to each scenario by the city managers. This work contributes to a new method whose objective is the development of a Sentiment Analysis System to Assist in the Management of Smart Cities (ASCI). This System is capable of capturing, classifying, processing, filtering by Domain of Interest and evaluating the sentiments of Smart City citizens. The method uses two Data Mining phases, one for the classification of Domains of Interest and the other for Sentiment Analysis. For the case study, the ASCI method was implemented, through which information was collected from a regional population in São Paulo city through Twitter Social Network data. A study of Sentiment Analysis in specific Domain of Interest Transport was also carried out, in which Linear SVC, Logistic Regression, Multinomial Naive Bayes and Random Forest classifiers were used to identify the positive, neutral and negative sentiments of collected tweets. The data were evaluated using two techniques of extraction of text characteristics: Bag of Words and TF-IDF. The ASCI method developed in this Thesis contributes significantly to the area of Sentiment Analysis and the results obtained were satisfactory when applied in Smart City Domain of Interest scenarios.

Social media offers a powerful outlet for people's thoughts and feelings -- it is an enormous ever-growing source of texts ranging from everyday observations to involved discussions. This thesis contributes to the field of sentiment analysis, which aims to extract emotions and opinions from text. A basic goal is to classify text as expressing either positive or negative emotion. Sentiment classifiers have been built for social media text such as product reviews, blog posts, and even Twitter messages. With increasing complexity of text sources and topics, it is time to re-examine the standard sentiment extraction approaches, and possibly to re-define and enrich sentiment definition. Thus, this thesis begins by introducing a rich multi-dimensional model based on Affect Control Theory and showing its usefulness in sentiment classification. Next, unlike sentiment analysis research to date, we examine sentiment expression and polarity classification within and across various social media streams by building topical datasets. When comparing Twitter, reviews, and blogs on consumer product topics, we show that it is possible, and sometimes even beneficial, to train sentiment classifiers on text sources which are different from the target text. This is not the case, however, when we compare political discussion in YouTube comments to Twitter posts, demonstrating the difficulty of political sentiment classification. We further show that neither discussion volume or sentiment expressed in these streams correspond well to national polls, putting in question recent research linking the two. The complexity of political discussion also calls for a more specific re-definition of "sentiment" as agreement with the author's political stance. We conclude that sentiment must be defined, and tools for its analysis designed, within a larger framework of human interaction.

The main purpose of this paper is to investigate the effect (if any) of investor sentiment on asset prices. To calibrate the ability of various market sentiment variables in forecasting stock returns, we followed the recursive regression methodology by Pesaran and Timmermann (1995,2000), taking into account the influences of regime switches on trading decisions of investors in real time. Our results suggest that stock returns may be difficult to predict when stock market is relatively unstable and investors are unsure of which forecasting model to be employed for trading strategies. This finding is not consistent with the empirical results of Pesran and Timmermann (1995). We also find that net buy (sell) of investment trusts and security dealers become in a close relation with stock returns after 1998, implying that institutional investors seem to reasonably capture the sentiment of the market and their trading strategies may reflect information asymmetries between managers and investors.

Cette thèse se propose d'analyser empiriquement la croyance en la méritocratie et les effets de l'éducation sur cette croyance. Elle s'articule autour de trois axes : – Mesurer l'emprise de la méritocratie scolaire – Questionner la légitimité perçue de la méritocratie scolaire – Explorer les déterminants de la croyance en la méritocratie, en particulier l'impact du diplôme. Pour ce faire, trois types de matériaux empiriques ont été mobilisés : (1) les données de l'International Social Survey Program (ISSP – Social Inequalities III 1999), (2) une enquête originale par questionnaires auprès d'étudiants de filières variées en première année d'études supérieures dans l'académie de Caen et (3) des entretiens réalisés auprès d'élèves de classes préparatoires parisiennes prestigieuses. Il apparaît, au terme de cette recherche, que bien que le mérite soit un principe fréquemment mobilisé par les acteurs sociaux, la méritocratie scolaire est quant à elle – notamment en France – davantage critiquée. Le diplôme est en effet perçu comme un critère assez peu légitime de différenciation sociale parce qu'il ne mesure qu'imparfaitement la volonté individuelle de réussir. Le niveau scolaire agit de manière contradictoire sur ces représentations : si les plus diplômés ont plus souvent l'impression que la société est méritocratique (effet positionnel du diplôme), ils n'en demeurent pas moins critiques sur capacité du diplôme à refléter les mérites de chacun, parce qu'ils sont conscients de la présence d'inégalités sociales à l'école (effet cognitif du diplôme). Cette recherche montre également qu'au-delà du titre scolaire mesuré de manière objective, c'est aussi l'expérience scolaire qui influe sur les représentations. Outre cet effet relatif du diplôme, un effet plus macrosocial des systèmes éducatifs sur les représentations de la méritocratie a été mis en évidence dans cette étude : la présence d'inégalités sociales à l'école ou plus généralement dans la société est notamment susceptible de rendre les individus plus attachés à la reconnaissance du diplôme.

Les émotions, les sentiments, les affects sont des thématiques encore peu explorées dans le champ des sciences de l'espace, pourtant de plus en plus nombreux sont les auteurs qui soulignent la nécessité de surmonter la difficulté de leur intégration. Souscrivant à cet objectif, la recherche pose comme hypothèse que la dimension affective de la relation de l'homme à son environnement, son rapport affectif à l'espace, depuis les mécanismes qui président à sa construction jusqu'à ses conséquences pratiques, constitue une connaissance utile à l'aménagement. À travers la thèse nous avons donc cherché à mettre en lumière les mécanismes de type affectif, en lien avec les valeurs, les préférences, qui sont en mesures d'intervenir à la fois sur les représentations, les décisions et sur les actions qui participent aussi bien des logiques géographiques au fondement de l'agencement des espaces que des logiques projectives propres aux pratiques de transformation intentionnelle des espaces habités.

How do people determine satisfaction in their relationships? One way may be to engage in bottom-up processing and rely on sexual satisfaction to arrive at an overall evaluation of the relationship. Another way may be to engage in top-down processing and allow the overall relationship satisfaction to color the perceptions of sexual satisfaction. The current study more rigorously examined the causal relationship between sexual and marital satisfaction through multilevel cross-lagged regression analyses of 8 waves of marital and sexual satisfaction reported by 72 newlywed couples over the first five years of marriage. Consistent with bottom-up processing, initial sexual satisfaction predicted subsequent marital satisfaction. Also, consistent with top-down processing, initial marital satisfaction predicted subsequent sexual satisfaction. The current findings extend theoretical perspectives on the relationship between sexual satisfaction and suggest that both causal paths be considered in future research and clinical practice.