Dissertations / Theses on the topic 'Sentiment analytics'

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

We report our investigation of how news stories influence the behaviour of tradable financial assets, in particular, equities. We consider the established methods of turning news events into a quantifiable measure and explore the models which connect these measures to financial decision making and risk control. The study of our thesis is built around two practical, as well as, research problems which are determining trading strategies and quantifying trading risk. We have constructed a new measure which takes into consideration (i) the volume of news and (ii) the decaying effect of news sentiment. In this way we derive the impact of aggregated news events for a given asset; we have defined this as the impact score. We also characterise the behaviour of assets using three parameters, which are return, volatility and liquidity, and construct predictive models which incorporate impact scores. The derivation of the impact measure and the characterisation of asset behaviour by introducing liquidity are two innovations reported in this thesis and are claimed to be contributions to knowledge. The impact of news on asset behaviour is explored using two sets of predictive models: the univariate models and the multivariate models. In our univariate predictive models, a universe of 53 assets were considered in order to justify the relationship of news and assets across 9 different sectors. For the multivariate case, we have selected 5 stocks from the financial sector only as this is relevant for the purpose of constructing trading strategies. We have analysed the celebrated Black-Litterman model (1991) and constructed our Bayesian multivariate predictive models such that we can incorporate domain expertise to improve the predictions. Not only does this suggest one of the best ways to choose priors in Bayesian inference for financial models using news sentiment, but it also allows the use of current and synchronised data with market information. This is also a novel aspect of our work and a further contribution to knowledge.

Both literature and practice have looked at different strategies to diminish healthcare associated costs. As an extension to this stream of research, the present three paper dissertation addresses the issue of reducing elevated healthcare costs using analytics. The first paper looks at extending the benefits of auditing algorithms from mere detection of fraudulent providers to maximizing the deterrence from inappropriate behavior. Using the structure of the physicians' network, a new auditing algorithm is developed. Evaluation of the algorithm is performed using an agent-based simulation and an analytical model. A case study is also included to illustrate the application of the algorithm in the warranty domain. The second paper relies on experimental data to build a personalized medical recommender system geared towards re-enforcing price-sensitive prescription behavior. The study analyzes the impact of time pressure, and procedure cost and prescription prevalence/popularity on the physicians' use of the system's recommendations. The third paper investigates the relationship between patients' compliance and healthcare costs. The study includes a survey of the literature along with a longitudinal analysis of patients' data to determine factors leading to patients' non-compliance, and ways to alleviate it.

This thesis examines the effect of coronavirus-related sentiment on Swedish stock market returns during the coronavirus pandemic. We study returns on the large cap and small cap price indices OMXSLCPI and OMXSSCPI during the period January 2, 2020 – April 30, 2020. Coronavirus sentiment proxies are constructed from news articles clustered into topics using latent Dirichlet allocation and scored through sentiment analysis. The impact of the sentiment proxies on the stock indices is then measured using a dynamic multiple regression model. The results show that the proxies representing fundamental changes in our model — Swedish Politics and Economic Policy — have a strongly significant impact on the returns of both indices, which is consistent with financial theory. We also find that sentiment proxies Sport and Coronavirus Spread are statistically significant and impact Swedish stock prices. This implies that coronavirus-related news influenced market sentiment in Sweden during the research period and could be exploited to uncover arbitrage. Finally, the amount of sentiment-inducing news published daily is shown to have an impact on stock price volatility.
Denna studie undersöker den effekt coronavirus-relaterat sentiment haft på avkastningen på svenska aktieindex under coronaviruspandemin. Vi studerar avkastningen på large cap- och small cap-prisindexen OMXSLCPI och OMXSSCPI under perioden 2 januari 2020 – 30 april 2020. Proxier för coronavirus-sentiment konstrueras från nyhetsartiklar som klustrats i ämnen genom latent Dirichlet-allokering och poängsatts genom sentimentanalys. Sentimentproxiernas påverkan på aktieindexen mäts sedan med en dynamisk multipel regressionsmodell. Resultaten visar att proxierna som representerar fundamentala förändringar i vår modell — svensk politik och ekonomisk policy — har en starkt signifikant inverkan på avkastningen på båda indexen, vilket är konsekvent med finansiell teori. Vi finner även att sentimentproxierna sport och spridning av coronaviruset är statistiskt signifikanta i sin påverkan på svenska aktiepriser. Detta innebär att coronavirus-relaterade nyheter påverkade marknadssentiment i Sverige under undersökningsperioden och skulle kunna användas för att upptäcka arbitrage. Slutligen visas mängden sentimentframkallande nyheter publicerade per dag ha en inverkan på aktieprisvolatilitet.

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Gvdasa - Inteligência Educacional
A temática deste trabalho estabelece um diálogo entre as áreas da educação a distância, linguística e processamento automático das línguas naturais (PLN). A proposta é responder às seguintes questões norteadoras: (i) como a negação da emoção se manifesta na superfície da língua? E (ii) que regras computacionais expressam a negação da emoção?. A metodologia do trabalho segue o proposto por Dias-da-Silva (2006), que organiza os trabalhos em PLN em três domínios de investigação complementares: (i) linguístico, (ii) linguístico-computacional e (iii) computacional. No primeiro domínio, o linguístico, descreve-se o fenômeno da negação e o seu uso. No domínio linguístico-computacional, vamos representar os padrões percebidos para orientar os especialistas a codificarem essas regras em uma linguagem computacional. Para propor a descrição linguístico-computacional dos modos de expressão da negação, a partir de um corpus construído em contexto de ensino a distância com base nos relatos diários e fóruns dos alunos, utilizamos como base a teoria abordada por Maria Helena de Moura Neves (2011). A etapa computacional, que prevê a implementação do sistema, é própria do informata e não será contemplada neste trabalho, será realizada por grupo de pesquisa parceiro em colaboração com a empresa GVDasa. Ao todo foram criadas 11 regras linguístico-computacionais que possibilita dar conta das propriedades linguísticas identificadas ao responder a questão (i) de pesquisa. As regras visam a contribuir para que um sistema computacional possa localizar os fenômenos da negação em textos e verificar a existência de inversões de polaridade e emoção.
The thematic of this work establishes a dialogue between the fields of distance learning, linguistics, and natural language processing (NLP). The proposal is to answer the following guiding questions: (i) how does the negation of emotion manifest itself on the surface of the language? and (ii) which computational rules express the negation of emotion? The methodology of this work follows the proposed by Dias-da-Silva (2006), who organizes the works in NLP in three complementary domains of investigation: (i) linguistics, (ii) computational-linguistics, and (iii) computational. In the first domain, the linguistic domain, the phenomenon of denial and its use is described. In the linguistic-computational domain, we will represent the perceived patterns in order to guide the experts to encode these rules in computational language. In order to propose the linguistic-computational description of the forms of expression of negation, through a corpus built in a distance learning context based on daily reports and students’ forums, we take as a base the theory approached by Maria Helena de Moura Neves (2011). The computational phase which forecasts the implementation of the system is pertinent to the computing technician and it will not be contemplated in this work, but it will be performed by a partner research group in collaboration with the GVDasa company. Altogether, 11 linguistic-computational rules were created that make it possible to account for the linguistic properties identified when answering the research question (i). The rules aim to contribute with a computational system to locate the phenomenon of negation in texts and verify the existence of inversions of polarity and emotion.

The influence of Information and Communication Technologies (ICTs) particularly internet technology has had a fundamental impact on the way government is administered, provides services and interacts with citizens. Currently, the use of social media is no longer limited to informal environments but is an increasingly important medium of communication between citizens and governments. The extensive and increasing use of social media will continue to generate huge amounts of user-generated content known as Big Social Data (BSD). The growing body of BSD presents innumerable opportunities as well as challenges for local government planning, management and delivery of public services to citizens. However, the governments have not yet utilised the potential of BSD for better understanding the public and gaining new insights from this new way of interactions. Some of the reasons are lacking in the mechanism and guidance to analyse this new format of data. Thus, the aim of this study is to evaluate how the body of BSD can be mined, analysed and applied in the context of local government in the UK. The objective is to develop a Big Social Data Analytics (BSDA) model that can be applied in the case of local government. Data generated from social media over a year were collected, collated and analysed using a range of social media analytics and network analysis tools and techniques. The final BSDA model was applied to a local council case to evaluate its impact in real practice. This study allows to better understand the methods of analysing the BSD in the public sector and extend the literature related to e-government, social media, and social network theory
Universiti Utara Malaysia

L’augmentation massive du volume de données générées chaque jour par les individus sur Internet offre aux chercheurs la possibilité d’aborder la question de la prédictibilité des marchés financiers sous un nouvel angle. Sans prétendre apporter une réponse définitive au débat entre les partisans de l’efficience des marchés et les chercheurs en finance comportementale, cette thèse vise à améliorer notre compréhension du processus de formation des prix sur les marchés financiers grâce à une approche Big Data. Plus précisément, cette thèse porte sur (1) la mesure du sentiment des investisseurs à fréquence intra-journalière, et le lien entre le sentiment des investisseurs et les rendements agrégés du marché,(2) la mesure de l’attention des investisseurs aux informations économiques et financières en temps réel, et la relation entre l’attention des investisseurs et la dynamique des prix des actions des sociétés à forte capitalisation, et enfin, (3) la détection des comportements suspicieux pouvant amoindrir le rôle informationnel des marchés financiers, et le lien entre le volume d’activité sur les réseaux sociaux et le prix des actions des entreprises de petite capitalisation. Le premier essai propose une méthodologie permettant de construire un nouvel indicateur du sentiment des investisseurs en analysant le contenu des messages publiés sur le réseau social Stock-Twits. En examinant les caractéristiques propres à chaque utilisateur (niveau d’expérience, approche d’investissement, période de détention), cet essai fournit des preuves empiriques montrant que le comportement des investisseurs naïfs, sujets à des périodes d’excès d’optimisme ou de pessimisme, a un impact sur la valorisation du marché action, et ce en accord avec les théories de la finance comportementale. Le deuxième essai propose une méthodologie permettant de mesurer l’attention des investisseurs aux informations en temps réel, en combinant les données des médias traditionnels avec le contenu des messages envoyés par une liste d’experts sur la plateforme Twitter. Cet essai démontre que lorsqu’une information attire l’attention des investisseurs, les mouvements de marchés sont caractérisés par une forte hausse des volumes échangés, une hausse de la volatilité et des sauts de prix. Cet essai démontre également qu’il n’y a pas de fuite d’information significative lorsque les sources d’informations sont combinées pour corriger un potentiel problème d’horodatage. Le troisième essai étudie le risque de manipulation informationnelle en examinant un nouveau jeu de données de messages publiés sur Twitter à propos des entreprises de petite capitalisation. Cet essai propose une nouvelle méthodologie permettant d’identifier les comportements anormaux de manière automatisée en analysant les interactions entre les utilisateurs. Étant donné le grand nombre de recommandations suspicieuses d’achat envoyées par certains groupes d’utilisateurs, l’analyse empirique et les conclusions de cet essai soulignent la nécessité d’un plus grand contrôle par les régulateurs de l’information publiée sur les réseaux sociaux ainsi que l’utilité d’une meilleure éducation des investisseurs individuels
The massive increase in the availability of data generated everyday by individuals on the Internet has made it possible to address the predictability of financial markets from a different perspective. Without making the claim of offering a definitive answer to a debate that has persisted for forty years between partisans of the efficient market hypothesis and behavioral finance academics, this dissertation aims to improve our understanding of the price formation process in financial markets through the use of Big Data analytics. More precisely, it analyzes: (1) how to measure intraday investor sentiment and determine the relation between investor sentiment and aggregate market returns, (2) how to measure investor attention to news in real time, and identify the relation between investor attention and the price dynamics of large capitalization stocks, and (3) how to detect suspicious behaviors that could undermine the in-formational role of financial markets, and determine the relation between the level of posting activity on social media and small-capitalization stock returns. The first essay proposes a methodology to construct a novel indicator of investor sentiment by analyzing an extensive dataset of user-generated content published on the social media platform Stock-Twits. Examining users’ self-reported trading characteristics, the essay provides empirical evidence of sentiment-driven noise trading at the intraday level, consistent with behavioral finance theories. The second essay proposes a methodology to measure investor attention to news in real-time by combining data from traditional newswires with the content published by experts on the social media platform Twitter. The essay demonstrates that news that garners high attention leads to large and persistent change in trading activity, volatility, and price jumps. It also demonstrates that the pre-announcement effect is reduced when corrected newswire timestamps are considered. The third essay provides new insights into the empirical literature on small capitalization stocks market manipulation by examining a novel dataset of messages published on the social media plat-form Twitter. The essay proposes a novel methodology to identify suspicious behaviors by analyzing interactions between users and provide empirical evidence of suspicious stock recommendations on social media that could be related to market manipulation. The conclusion of the essay should rein-force regulators’ efforts to better control social media and highlights the need for a better education of individual investors

Classification of sentiment on customer reviews is a real-world application for many companies that offer text analytics and opinion extraction on customer reviews on different domains such as consumer electronics, hotels, restaurants, and car rental agencies. Natural Language Processing’s latest progress has seen the development of many new state-of-the-art approaches for representing the meaning of sentences, phrases, and words in the text using vector space models, so-called embeddings. In this thesis, we evaluated the most current and most popular text representation techniques against traditional methods as a baseline. The evaluation dataset consists of customer reviews from different domains with different lengths used by a text analysis company. Through a train dataset exploration, we evaluated which datasets were the most suitable for this specific task. Furthermore, we explored different techniques that could be used to alter a language model’s decisions without retraining it. Finally, all the methods were evaluated against their time performance and the resource requirements to present an overall experimental assessment that could potentially help the company decide which is the most appropriate technique to replace its system in a production environment.
Klassificeringen av attityd och känsloläge i kundrecensioner är en tillämpning med praktiskt värde för flera företag i marknadsanalysbranschen. Aktuell forskning i språkteknologi har etablerat vektorrum som standardrepresentation för ord, fraser och yttranden, så kallade embeddings. Denna uppsats utvärderar den senaste tidens mest framgångsrika textrepresentationsmodeller jämfört med mer traditionella vektorrum. Utvärdering görs genom att jämföra automatiska analyser med mänskliga bedömningar för kundrecensioner av varierande längd från olika domäner tillhandahållna av ett textanalysföretag. Inom ramen för studien har olika testmängder jämförts och olika sätt att modifera en språkmodells klassficering utan om träning. Alla modeller har också jämförts med avseende på resurs- och tidsåtgång för träning för att hjälpa uppdragsgivaren fatta beslut om vilken teknik som utgör den mest ändamålsenliga utvecklingsvägen för dess driftsatta system.

In the last decade, development of Information and Communication Technology (ICT) thatsupports online learning has increased the demand for e-learning and Massive Open OnlineCourses (MOOCs). Despite their increased popularity, MOOCs are struggling with highdropout rates and only a small percentage of learners complete the courses they enrolled in. Thepurpose of this thesis is to gain knowledge about MOOC learner behaviour. The aim of thestudy is to identify the motivations of learners and how these differ between learners whocompleted a course and those who dropped out. Research on MOOC learners has mostly beencarried out using a quantitative approach. While quantitative methodologies are effective inhandling the large amount of data produced by MOOCs, qualitative methods can give deeperinsights into online learners’ motivations. Therefore, this thesis employs an explanatorysequential mixed methods research, in which sentiment analysis and topic modeling of learnerreviews from the platform Coursera are further explained by qualitative interviews with MOOClearners. In the study 28,000 reviews scraped from five courses within the fields of data sciencewere analyzed and ten interviews were held with learners who either completed, dropped outfrom or both completed and dropped out from a MOOC. In the quantitative analysis nine coursefactors were found that learners wrote about: content, delivery, assessment, learning experience,tools, video material, teaching style, instructor skills and course provider. In addition, eighteenthemes were yielded from the interviews: self-discipline, just for fun, certificates, personaldevelopment, knowledge, career, time, equipment, practical exercise, interaction, instructor,reality, structure, external material, cost, community, degree of difficulty and other. In thediscussion the empirical findings are reflected upon using the theoretical framework of theresearch and the literature review. The result does not reveal any differences in motivationsbetween learners who completed a course and those who dropped out, however, it does identifyfactors that caused learners’ to drop out and the topics that most negative learner reviews wereabout. This research contributes to the body of knowledge in the field of research on MOOClearner retention and motivations. The topic is relevant for research in education informaticsand for continued improvements in delivery of MOOCs.

There is a growing volume of global data, which is offering new possibilities for those market participants, who know to take advantage of it. Data, information and knowledge are new highly regarded commodity especially in the banking industry. Traditional data analytics is intended for processing data with known structure and meaning. But how can we get knowledge from data with no such structure? The thesis focuses on Big Data analytics and its use in banking and financial industry. Definition of specific applications in this area and description of benefits for international and Czech banking institutions are the main goals of the thesis. The thesis is divided in four parts. The first part defines Big Data trend, the second part specifies activities and tools in banking. The purpose of the third part is to apply Big Data analytics on those activities and shows its possible benefits. The last part focuses on the particularities of Czech banking and shows what actual situation about Big Data in Czech banks is. The thesis gives complex description of possibilities of using Big Data analytics. I see my personal contribution in detailed characterization of the application in real banking activities.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
The present study aimed at observing the perceptions, feelings and fantasies of patients in relation to their symptoms of psychogenic excoriation. The population consisted of 48 people, 28 of the control group and 20 of the experimental group, referred by the city clinic of the University of Mogi das Cruzes. Both groups answered a form of characterization and Beck Anxiety Inventory. The second group also answered a semi structured interview, the technical drawing of the human figure, guided imagery and thematic design. The method of analysis of the results was quantitative for anxiety and the form of characterization in both groups, and qualitative analysis was intended only to other techniques applied in the experimental group. The analysis was based on assumptions of Analytical Psychology. It was concluded that stressful situations and conflict contributed to the development and aggravation of symptoms and it was observed that feelings of abandonment, neglect and emptiness arising from childhood surfaced for many participants, in which we found evidence of maternal and paternal complexes. A multidisciplinary approach is necessary and psychological intervention is of paramount importance for the development of traumatic content
pacientes quanto a seus sintomas de escoriação psicogênica. A população foi composta de 48 pessoas, sendo 28 do grupo controle e 20 do experimental, indicadas pela Policlínica da Universidade de Mogi das Cruzes. Ambos os grupos responderam ao formulário de caracterização e ao inventário de Beck de ansiedade, sendo que o segundo também respondeu à entrevista semiestruturada, participou das técnicas do desenho da figura humana, da imaginação dirigida e do desenho temático. O método de análise foi quantitativo para os resultados de ansiedade e caracterização da amostra nos dois grupos, e a análise qualitativa se destinou às demais técnicas aplicadas somente no grupo experimental. A análise dos resultados foi baseada em pressupostos da Psicologia Analítica. Concluiu-se que situações estressantes e de conflito contribuíram para o aparecimento e o agravamento dos sintomas e observou-se que sentimentos de abandono, negligência e vazio advindos da infância vieram à tona por muitos participantes, estando presentes evidências dos complexos materno e paterno. Uma atuação multidisciplinar se faz necessária e a intervenção psicológica é de suma importância para elaboração dos conteúdos traumáticos

碩士
國立政治大學
會計學系
107
With the rapid development of information technology, the Internet has become the fastest and most convenient channel for investors to obtain information. Different from previous investors who made decisions based on investment reports or financial reports. Nowadays investors also based on the "Financial News" further makes investment decisions. Financial news can provide investors with instant information, therefore, plays an important role in delivering information to investors. The main purpose of this study is to explore whether the "sentiment" impact of "Financial News" on investors is closely related to "System Risk". This study assumes that "News Sentiment Analysis" is related to System Risk. Using the financial news related to 51 listed companies in the "Taiwan 50 Index constituents" in the fourth quarter of 2018, with the "Financial Sentiment Dictionary" to collect "Positive and Negative sentimental words" in financial news, it is used to calculate the variables of "Sentimental Tone" and "Quantity of News" related to each model. Finally, the "Multiple Linear Regression" is performed by SAS statistical software to test the relationship between financial News Sentiment Analysis and System Risk. The empirical results show that there is a correlation between "Sentimental Tone" and " Quantity of News" and System Risk, and there is a "Three-day periods" relation between News Sentiment Analysis and System Risk. This study also found that "Negative Sentimental Tone" and the sentiment analysis of "Negative News" has a significant correlation with System Risk.

碩士
靜宜大學
會計學系
107
Predicting the stock price has attracted much attention from academia and investor. Can the stock price be predicted? Numerous studies tried to use different ways to answer that. Behavioral economics tell us that sentiment can affect individual behavior and decision-making, so can investor sentiment predict the stock price? For answering this, here this study will use a Neural Network to prove the hypothesis that investor sentiment, as measure from Social Big Data (OpView), have predictive effects on the stock price prediction. The result shows that the investor sentiment measured by OpView have predictive effect of stock price prediction in Taiwan, and this effect will increase as the popularity of individual stocks rises.

A escrita é uma forma de registro da expressão humana. Durante a escrita são expressos fatos e opiniões, posteriormente armazenados em sistemas de informação. As plataformas de e-learning são ambientes educacionais de ensino à distância e exemplo de sistemas de informação que armazenam fatos e opiniões. Um caso específico de ambiente de e-learning é o MOOC (Massive Open Online Course). O objetivo desta pesquisa é combinar quatro API (Interface de programação de aplicativo) de análise de sentimento, nomeadas Amazon Comprehend, Google Natural Language, IBM Watson Natural Language Understanding e Microsoft Text Analytics e criar a média ponderada dos scores destas APIs para efetuar a análise da polaridade do sentimento em um ambiente MOOC. Outro objetivo desta pesquisa é estudar se existe relação entre a polaridade de sentimento expressa nas intervenções online e o aproveitamento acadêmico dos participantes em MOOC da Universidade Aberta de Portugal. A pesquisa cita e discute os trabalhos relacionados, classificados por abordagens técnicas. A solução é demonstrada com etapas bem definidas tanto do ponto de vista científico quanto do ponto de vista metodológico. É utilizada uma visão científica positivista, com análise racional e qualitativa de dados experimentais, coletados de sistemas de informação e aplicados a um ambiente já operacional. A pesquisa é avaliada em termos de revocação (recall) e precisão da análise algorítmica de sentimentos, confrontada com sentimentos classificados de forma qualitativa por humanos. As conclusões apontam que o método de análise combinada da polaridade de sentimento é válido para efetuar medidas precisas de polaridade de sentimento no ambiente MOOC e que não existe relação direta entre a polaridade de sentimento e o aproveitamento acadêmico dos participantes em MOOC da Universidade Aberta de Portugal.
Writing is a way of recording human expression. During writing, facts and opinions are expressed and stored in information systems. E-learning platforms are educational environments of distance learning and examples of information systems that store facts and opinions. A specific case of e-learning environment is the MOOC (Massive Open Online Course). The objective of this research is to combine four sentiment analysis API (Application program interface), described by Amazon Natural Language, Google Natural Language, IBM Watson Natural Language Understanding, and Microsoft Text Analytics to create a weighted average of these API scores to analyze the polarity of sentiments in a MOOC environment. Another objective of this research is to study if exists the relationship between the polarity of sentiment expressed in the online interventions and the academic achievement of the participants in the Universidade Aberta de Portugal.. The research cites and discusses the related works, classified by technical approaches. The solution is demonstrated with well-defined steps from both the scientific and methodological point of view. A positivist scientific view is used, with rational and qualitative analysis of experimental data, collected from information systems and applied to an already operational environment. The research is evaluated in terms of recall and precision of the algorithmic sentiment analysis, confronted with sentiment classified qualitatively by humans. The conclusions indicate that the combined polarity of sentiment analysis method is valid for performing accurate polarity of sentiment measurements in the MOOC environment and there is no direct relationship between polarity of sentiment and academic achievement of participants from Universidade Aberta de Portugal MOOC´s.

abstract: Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable - in fact, the de facto - virtual town halls for people to discover, report, share and communicate with others about various types of events. These events range from widely-known events such as the U.S Presidential debate to smaller scale, local events such as a local Halloween block party. During these events, we often witness a large amount of commentary contributed by crowds on social media. This burst of social media responses surges with the "second-screen" behavior and greatly enriches the user experience when interacting with the event and people's awareness of an event. Monitoring and analyzing this rich and continuous flow of user-generated content can yield unprecedentedly valuable information about the event, since these responses usually offer far more rich and powerful views about the event that mainstream news simply could not achieve. Despite these benefits, social media also tends to be noisy, chaotic, and overwhelming, posing challenges to users in seeking and distilling high quality content from that noise. In this dissertation, I explore ways to leverage social media as a source of information and analyze events based on their social media responses collectively. I develop, implement and evaluate EventRadar, an event analysis toolbox which is able to identify, enrich, and characterize events using the massive amounts of social media responses. EventRadar contains three automated, scalable tools to handle three core event analysis tasks: Event Characterization, Event Recognition, and Event Enrichment. More specifically, I develop ET-LDA, a Bayesian model and SocSent, a matrix factorization framework for handling the Event Characterization task, i.e., modeling characterizing an event in terms of its topics and its audience's response behavior (via ET-LDA), and the sentiments regarding its topics (via SocSent). I also develop DeMa, an unsupervised event detection algorithm for handling the Event Recognition task, i.e., detecting trending events from a stream of noisy social media posts. Last, I develop CrowdX, a spatial crowdsourcing system for handling the Event Enrichment task, i.e., gathering additional first hand information (e.g., photos) from the field to enrich the given event's context. Enabled by EventRadar, it is more feasible to uncover patterns that have not been explored previously and re-validating existing social theories with new evidence. As a result, I am able to gain deep insights into how people respond to the event that they are engaged in. The results reveal several key insights into people's various responding behavior over the event's timeline such the topical context of people's tweets does not always correlate with the timeline of the event. In addition, I also explore the factors that affect a person's engagement with real-world events on Twitter and find that people engage in an event because they are interested in the topics pertaining to that event; and while engaging, their engagement is largely affected by their friends' behavior.
Dissertation/Thesis
Doctoral Dissertation Computer Science 2014

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Nos dias correntes, é visível uma crescente utilização das redes sociais, onde as pessoas podem expressar a sua opinião sobre o que sentem relativamente às empresas, aos seus produtos e/ou serviços. Tal facto apresenta uma oportunidade para as empresas entenderem o que+ se fala sobre elas e se tal é positivo ou negativo (Santos & Ramos, 2009). A crescente utilização das redes sociais levou ao aparecimento do Marketing Digital, onde se tenta captar a atenção das pessoas no meio digital. As redes sociais têm um papel essencial neste mesmo, sendo um dos principais canais utilizados para a marca interagir com o público, onde, por exemplo, em campanhas de maior dimensão podem ser realizadas publicações por forma a captar a atenção das pessoas, havendo a necessidade de haver uma análise da performance destas campanhas no meio digital. Como tal, neste projeto, tendo em conta a importância do digital no Marketing, foram extraídos e analisados os dados da empresa JUMIA (empresa de e-commerce) da Nigéria no Facebook, sendo realizadas uma análise de sentimentos e deteção de tópico às duas campanhas de maior dimensão, tendo como objetivo entender qual o sentimento e temática associados a estes mesmos comentários, por forma a analisar a performance das campanhas e a dar recomendações.
There is a growing use of social media in everyday life, where people can express their opinion about what they feel about companies and their products and/or services. This is an opportunity for companies to understand what is said about them and whether this is positive or negative (Santos & Ramos, 2009). The growing use of social media has led to the emergence of Digital Marketing, where companies try to capture people's attention in the digital environment, with social networks being one of the main channels used for the brand to interact with the public. Posts can be carried out in order to capture people’s attention and because of that there should be an analysis of the performance of these campaigns in the digital environment. As such, this project was carried out taking into account the importance of the digital in Marketing. The data of all the posts and comments in JUMIA (e-commerce company) in Nigeria on Facebook were extracted and analyzed, and a sentiment analysis and topic detection were performed at the two campaigns of larger dimension, aiming to understand the feeling and thematic associated to these comments, in order to analyze the performance of the campaigns and to give recommendations.

Yes
This exploratory research aims to investigate social media users’ expectations of information systems (IS) products that are conceived but not yet launched. It specifically analyses social media data from Twitter about forthcoming smartphones and smartwatches from Apple and Samsung, two firms known for their innovative gadgets. Tweets related to the following four forthcoming IS products were retrieved from 1st January 2020 to 30th September 2020: (1) Apple iPhone 12 (6,125 tweets), (2) Apple Watch 6 (553 tweets), (3) Samsung Galaxy Z Flip 2 (923 tweets), and (4) Samsung Galaxy Watch Active 3 (207 tweets). These 7,808 tweets were analysed using a combination of the Natural Language Processing Toolkit (NLTK) and sentiment analysis (SentiWordNet). The online community was quite vocal about topics such as design, camera and hardware specifications. For all the forthcoming gadgets, the proportion of positive tweets exceeded that of negative tweets. The most prevalent sentiment expressed in Apple-related tweets was neutral but in Samsung-related tweets was positive. Additionally, it was found that the proportion of tweets echoing negative sentiment was lower for Apple compared with Samsung. This paper is the earliest empirical work to examine the degree to which social media chatter can be used by project managers for IS development projects, specifically for the purpose of end-users’ expectation management.

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
As an insurance company, Ageas Portugal has lots of data related to their customers. Usually, most of data used by companies (disregarding few companies that already use advanced machine learning and artificial intelligence techniques) are structured data, that are known as formatted datasets and tables with customer information. But, with the advance of technology, more companies are starting to use their unstructured data, which could be helpful to find insights and achieve goals. From the different data sources in human language form the company has as emails, customer surveys, medical transcriptions and etc., we have agreed an email database would be the best option for the project development. This type of data requires a very thorough data preparation as there are irrelevant parts within emails as signatures and disclaimers, which should be excluded. Analyzing customer’s interaction with the company we could find insights about how to increase sales and reduce churn rate. We have applied two Text Mining techniques (Sentiment Analysis and Topic Classification) and a proof of concept was conducted. It showed that clients who send or are mentioned in emails tend to cancel their policies at higher rate than those without emails, even if the email’s topic is not related to cancellation. It has also showed that the effect of sentiment on cancellations behavior appears to be mixed, requiring further analysis. The full project was developed in Python but there was also a comparison with other market solutions as Amazon Web Services, SAS, Google Cloud and Microsoft Azure, in order to find the best Text Mining tool to fit with the company. As expected, Python was elected as the best option.

Yes
The governments, nowadays, throughout the world are increasingly becoming dependent on public opinion regarding the framing and implementation of certain policies for the welfare of the general public. The role of social media is vital to this emerging trend. Traditionally, lack of public participation in various policy making decision used to be a major cause of concern particularly when formulating and evaluating such policies. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. Cloud-based e-governance is currently being realized due to IT infrastructure availability along with mindset changes of government advisors towards realizing the various policies in a best possible manner. This paper presents a pragmatic approach that combines the capabilities of both cloud computing and social media analytics towards efficient monitoring and controlling of governmental policies through public involvement. The proposed system has provided us some encouraging results, when tested for Goods and Services Tax (GST) implementation by Indian government and established that it can be successfully implemented for efficient policy making and implementation.

A evolução das técnicas e aplicações do Business Intelligence and Analytics tem sido bastante elevada, como é o caso das interfaces de programação de aplicações que utilizam Inteligência Artificial direcionadas para análise de texto. O foco da análise de negócios não deve ser só a descoberta de dados relevantes, mas também a agilização de processos com a utilização e combinação de programação de aplicações direcionadas para esse efeito. O objetivo deste trabalho consistiu na realização de um estudo da aplicação de ferramentas de Business Analytics para a realização de análise sentimental, que agilizem e facilitem a sua utilização por utilizadores não especializados em Business Analytics. Para esse objetivo, foi selecionada uma das ferramentas mais utilizadas em Business Analytics, onde se destaca a facilidade de utilização, automação e realizada a aplicação da mesma a um exemplo prático de análise sentimental. Para melhor entendimento dos resultados e conclusões foi necessária a criação de um fluxo de processos, com carregamento, tratamento de texto e modelagem, com a abordagem clássica.