More and more events and anomalies that have happened in recent years cannot be explained by traditional models, which leads to a pervasive doubt of the effectiveness of the efficient market hypothesis. In particular, over ninety percent of Taiwan¡¦s stock market investors are individuals, and the noise trading phenomenon is very common and has a great effect upon the return of stock. Hence, the measure of investor sentiment formed by noise traders becomes a task for the researcher studying the factors which effect the stock return in Taiwan. The objective of this paper is to find the investor sentiment proxy variables which can be a significant factor in explaining stock return. This analysis adopts the arbitrage pricing model of the macroeconomic factors. The sample contains data for most listed stocks on the Taiwan Stock Exchange from 1984 to 2002. By combining the stock or company characteristic related to the noise traders¡¦ perception, including market value, stock and etc., and phenomenons effect by investor sentiment, including closed-end fund discount, initial returns on IPOs, and number of IPOs to the arbitrage pricing model , we found that closed-end fund discount and initial returns on IPOs are significant and appropriate to investor sentiment proxy variables. However, the number of IPOs is not significant enough

The success of on-line review websites has led to an overwhelming number of on-line consumer reviews. These reviews have become an important tool for consumers when making a decision to purchase a product. This growth has led to the need for applications that enable this information to be presented in a way that is meaningful. These applications often rely on domain specific semantic lexicons which are both expensive and time consuming to make. The following thesis proposes an unsupervised approach for product aspect discovery in on-line consumer reviews. We apply a two step hierarchical clustering process in which we first cluster based on the semantic similarity of the contexts of terms and then on the similarity of the hypernyms of the cluster members. The method also includes a process for assigning class labels to each of the clusters. Finally an experiment showing how the proposed methods can be used to measure aspect based sentiment is performed. The methods proposed in this thesis are evaluated on a set of 157,865 reviews from a major commercial website and found that the two-step clustering process increases cluster F-scores over a single round of clustering. Finally, the proposed methods are compared to a state of the art topic modelling approach by Titov and McDonald (2008).

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RIBEIRO, Andreh Sabino. Paixões propulsoras e razão diretiva na ciência moral de David Hume. 2010. 95f. – Dissertação (Mestrado) – Universidade Federal do Ceará, Programa de Pós-graduação em Filosofia, Fortaleza (CE), 2010. / Submitted by Gustavo Daher (gdaherufc@hotmail.com) on 2017-09-20T15:33:41Z No. of bitstreams: 1 2010_dis_asribeiro.pdf: 762851 bytes, checksum: 9cbc3a73ac39049b43717d921acad47d (MD5) / Approved for entry into archive by Márcia Araújo (marcia_m_bezerra@yahoo.com.br) on 2017-09-23T14:28:53Z (GMT) No. of bitstreams: 1 2010_dis_asribeiro.pdf: 762851 bytes, checksum: 9cbc3a73ac39049b43717d921acad47d (MD5) / Made available in DSpace on 2017-09-23T14:28:53Z (GMT). No. of bitstreams: 1 2010_dis_asribeiro.pdf: 762851 bytes, checksum: 9cbc3a73ac39049b43717d921acad47d (MD5) Previous issue date: 2010 / This work is intended to show that David Hume‟s moral philosophy associated reason to feeling, both in mental and social domains, like an inseparable compound in moral action and distinction. He believed that the artificiality of institutions did not implicate the negation of nature, but its extension. Thus, virtues and vices are recognized by humans as actions which respectively please and unplease them. This is because we share a nature in common that enables us to discern the utility of behavior for our survival according to the circumstances of time and space. Then, it means a refusal of the methaphysical objectivism and the religious authority as the foundation of morality. Hume understood his project as a complement to the Scientific Revolution of the seventeenth century, extending the use of experimental method in the field of morality. / Este trabalho pretende apresentar a filosofia moral de David Hume a partir da associação entre razão e sentimento, a formarem um composto inseparável na ação e na distinção morais. Para tanto, considero sua teoria no domínio mental e no social. O filósofo acreditava que a artificialidade das instituições não implicava a negação da natureza, mas sua extensão. Assim, virtudes e vícios são reconhecidos pelos seres humanos enquanto ações que, respectivamente, lhes agradam e desagradam. Isto porque compartilhamos uma mesma natureza que nos capacita discernirmos a utilidade das condutas para nossa sobrevivência de acordo com as circunstâncias de tempo e espaço. Recusa-se, então, um objetivismo metafísico e uma autoridade religiosa como fundamento da moralidade. Hume entendia seu projeto como um complemento da Revolução Científica do século XVII, ao estender o uso do método experimental no campo da moralidade.

abstract: In this thesis multiple approaches are explored to enhance sentiment analysis of tweets. A standard sentiment analysis model with customized features is first trained and tested to establish a baseline. This is compared to an existing topic based mixture model and a new proposed topic based vector model both of which use Latent Dirichlet Allocation (LDA) for topic modeling. The proposed topic based vector model has higher accuracies in terms of averaged F scores than the other two models. / Dissertation/Thesis / Masters Thesis Computer Science 2016

abstract: Cyberbullying is a phenomenon which negatively affects individuals. Victims of the cyberbullying suffer from a range of mental issues, ranging from depression to low self-esteem. Due to the advent of the social media platforms, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include use of standards and guidelines, human moderators, use of blacklists based on profane words, and regular expressions to manually detect cyberbullying. However, these mechanisms fall short in social media and do not scale well. Users in social media use intentional evasive expressions like, obfuscation of abusive words, which necessitates the development of a sophisticated learning framework to automatically detect new cyberbullying behaviors. Cyberbullying detection in social media is a challenging task due to short, noisy and unstructured content and intentional obfuscation of the abusive words or phrases by social media users. Motivated by sociological and psychological findings on bullying behavior and its correlation with emotions, we propose to leverage the sentiment information to accurately detect cyberbullying behavior in social media by proposing an effective optimization framework. Experimental results on two real-world social media datasets show the superiority of the proposed framework. Further studies validate the effectiveness of leveraging sentiment information for cyberbullying detection. / Dissertation/Thesis / Masters Thesis Computer Science 2017

abstract: Social media refers computer-based technology that allows the sharing of information and building the virtual networks and communities. With the development of internet based services and applications, user can engage with social media via computer and smart mobile devices. In recent years, social media has taken the form of different activities such as social network, business network, text sharing, photo sharing, blogging, etc. With the increasing popularity of social media, it has accumulated a large amount of data which enables understanding the human behavior possible. Compared with traditional survey based methods, the analysis of social media provides us a golden opportunity to understand individuals at scale and in turn allows us to design better services that can tailor to individuals’ needs. From this perspective, we can view social media as sensors, which provides online signals from a virtual world that has no geographical boundaries for the real world individual's activity. One of the key features for social media is social, where social media users actively interact to each via generating content and expressing the opinions, such as post and comment in Facebook. As a result, sentiment analysis, which refers a computational model to identify, extract or characterize subjective information expressed in a given piece of text, has successfully employs user signals and brings many real world applications in different domains such as e-commerce, politics, marketing, etc. The goal of sentiment analysis is to classify a user’s attitude towards various topics into positive, negative or neutral categories based on textual data in social media. However, recently, there is an increasing number of people start to use photos to express their daily life on social media platforms like Flickr and Instagram. Therefore, analyzing the sentiment from visual data is poise to have great improvement for user understanding. In this dissertation, I study the problem of understanding human sentiments from large scale collection of social images based on both image features and contextual social network features. We show that neither visual features nor the textual features are by themselves sufficient for accurate sentiment prediction. Therefore, we provide a way of using both of them, and formulate sentiment prediction problem in two scenarios: supervised and unsupervised. We first show that the proposed framework has flexibility to incorporate multiple modalities of information and has the capability to learn from heterogeneous features jointly with sufficient training data. Secondly, we observe that negative sentiment may related to human mental health issues. Based on this observation, we aim to understand the negative social media posts, especially the post related to depression e.g., self-harm content. Our analysis, the first of its kind, reveals a number of important findings. Thirdly, we extend the proposed sentiment prediction task to a general multi-label visual recognition task to demonstrate the methodology flexibility behind our sentiment analysis model. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2018

Un sentiment de solitude survient chez un individu lorsque ses relations interpersonnelles sont insatisfaisantes, que ce soit sur le plan quantitatif ou qualitatif. Considérant que la communication interpersonnelle, qui permet la création et le maintien des relations interpersonnelles, a grandement cours par l’entremise de technologies de communication via Internet, bon nombre de recherches se sont penchées sur les liens potentiels entre les usages de ces technologies et le sentiment de solitude. Les résultats de ces travaux sont toutefois très disparates. Ainsi, la présente recherche explore cette problématique en se concentrant spécifiquement sur un dispositif de communication en particulier : le site de réseaux sociaux Facebook. Les usages de celui-ci sont conceptualisés selon un ensemble cohérent d’approches et de notions théoriques, soit les usages et gratifications, les affordances des technologies ainsi que certaines notions empruntées à la sociologie des usages (contextes de pratiques, entrelacement des usages). En ce qui concerne le sentiment de solitude, l’approche multidimensionnelle des besoins sociaux est mobilisée. Au niveau méthodologique, une méthode mixte utilisant le sondage par questionnaire comme outil de collecte de données a été préconisée. Les résultats démontrent un entrelacement des usages des différentes fonctionnalités de Facebook, ainsi que des corrélations entre certaines utilisations spécifiques de Facebook et le sentiment de solitude, tant sociale qu’émotionnelle. Ces résultats viennent s’ajouter à ceux d’autres publications récentes, qui démontrent qu’il y a effectivement des relations complexes entre les usages de Facebook et le sentiment de solitude.

The goal of my master thesis is to describe the Automated Sentiment Analysis, its methods and Cross-domain problems and to test the already existing model. I have applied this model on the data from the Czech-Slovak film database website CSFD.cz, Czech e-shop MALL.cz and one of the biggest Czech websites about books Databazeknih.cz to contribute to the solution of the Cross-Domain issue by using n-grams and the analytic software RapidMiner.

A análise de sentimentos é um campo de estudo com recente popularização devido ao crescimento da Internet e do conteúdo que é gerado por seus usuários, principalmente nas redes sociais, nas quais as pessoas publicam suas opiniões em uma linguagem coloquial e em muitos casos utilizando de artifícios gráficos para tornar ainda mais sucintos seus diálogos. Esse cenário é observado no Twitter, uma ferramenta de comunicação que pode facilmente ser usada como fonte de informação para várias ferramentas automáticas de inferência de sentimentos. Esforços de pesquisas têm sido direcionados para tratar o problema de análise de sentimentos em redes sociais sob o ponto de vista de um problema de classificação, com pouco consenso sobre qual é o classificador com melhor poder preditivo, bem como qual é a configuração fornecida pela engenharia de atributos que melhor representa os textos. Outro problema é que em um cenário supervisionado, para a etapa de treinamento do modelo de classificação, é imprescindível se dispor de exemplos rotulados, uma tarefa árdua e que demanda esforço humano em grande parte das aplicações. Esta tese tem por objetivo investigar o uso de agregadores de classificadores (classifier ensembles), explorando a diversidade e a potencialidade de várias abordagens supervisionadas quando estas atuam em conjunto, além de um estudo detalhado da fase que antecede a escolha do classificador, a qual é conhecida como engenharia de atributos. Além destes aspectos, um estudo mostrando que o aprendizado não supervisionado pode fornecer restrições complementares úteis para melhorar a capacidade de generalização de classificadores de sentimento é realizado, fornecendo evidências de que ganhos já observados em outras áreas do conhecimento também podem ser obtidos no domínio em questão. A partir dos promissores resultados experimentais obtidos no cenário de aprendizado supervisionado, alavancados pelo uso de técnicas não supervisionadas, um algoritmo existente, denominado de C3E (Consensus between Classification and Clustering Ensembles) foi adaptado e estendido para o cenário semissupervisionado. Este algoritmo refina a classificação de sentimentos a partir de informações adicionais providas pelo agrupamento em um procedimento de autotreinamento (self-training). Tal abordagem apresenta resultados promissores e competitivos com abordagens que representam o estado da arte em outros domínios. / Sentiment analysis is a field of study that shows recent popularization due to the growth of Internet and the content that is generated by its users. More recently, social networks have emerged, where people post their opinions in colloquial and compact language. This is what happens in Twitter, a communication tool that can easily be used as a source of information for various automatic tools of sentiment inference. Research efforts have been directed to deal with the problem of sentiment analysis in social networks from the point of view of a classification problem, where there is no consensus about what is the best classifier, and what is the best configuration provided by the feature engineering process. Another problem is that in a supervised setting, for the training stage of the classification model, we need labeled examples, which are hard to get in the most of applications. The objective of this thesis is to investigate the use of classifier ensembles, exploring the diversity and the potential of various supervised approaches when these work together, as well as to provide a study about the phase that precedes the choice of the classifier, which is known as feature engineering. In addition to these aspects, a study showing that unsupervised learning techniques can provide useful and additional constraints to improve the ability of generalization of the classifiers is also carried out. Based on the promising results got in supervised learning settings, an existing algorithm called C3E (Consensus between Classification and Clustering Ensembles) was adapted and extended for the semi-supervised setting. This algorithm refines the sentiment classification from additional information provided by clusters of data, in a self-training procedure. This approach shows promising results when compared with state of the art algorithms.