Na WEB, são encontradas postagens sobre assuntos variados, notícias de celebridades, produtos e serviços. Tal conteúdo contém emoções positivas, negativas ou neutras. Minerar o sentimento da população sobre candidatos a eleições e seus aspectos em mídias virtuais pode ser realizado por meio de técnicas de Mineração de Opiniões. Existem soluções para fontes de opinião fortemente estruturadas, tais como revisões de produtos e serviços, no entanto o problema que se apresenta é realizar a mineração de opiniões em nível de aspecto em fontes de opiniões fracamente estruturadas. Além de avaliar conceitos relacionados à mineração de opiniões, o presente trabalho descreve a realização de um estudo de caso, o qual analisa fontes de opiniões fracamente estruturadas e propõe uma abordagem para minerar opiniões em nível de aspecto, utilizando como fontes de opinião comentários de leitores de jornais. O estudo de caso contribui (i) na concepção de uma abordagem para identificação da opinião em nível de aspecto sobre entidades eleitorais em comentários de notícias políticas, (ii) na aplicação de um método baseado em aprendizagem de máquina para classificar a opinião sobre entidades e seus aspectos em três classes (positivo, negativo e neutro), (iii) na representação da sumarização visual de opinião sobre entidades e seus aspectos. São descritos experimentos para identificar comentários que mencionam os aspectos saúde e educação, utilizando co-ocorrência, em que foram obtidos resultados satisfatórios utilizando as técnicas Expected Mutual Information Measure e phi-squared. Já para a polarização de sentenças, são realizados experimentos com duas abordagens de classificação: uma que classifica sentenças em três classes e outra que realiza classificações binárias em duas etapas. / In the WEB are found posts about various subjects like celebrity news, products and services. Such content has positive, negative or neutral emotions. Mining the population’s sentiments about elections candidates and their aspects in virtual media can be performed using Opinion Mining techniques. There are solutions for highly structured opinion sources, such as reviews of products and services, however the problem is how to perform aspect-based opinion mining in less structured opinions sources. Besides evaluating concepts related to opinion mining, this work describes a case study which analyzes weakly structured sources and proposes an approach to mine aspect-based opinions using as sources of sentiment reviews of newspaper readers. The case study contributes (i) designing an approach to identify the aspect-based opinion about electoral candidates in news political comments, (ii) to the application of a machine learning-based method to classify the opinion about entities and their aspects in three classes (positive, negative and neutral) (iii) to the representation of a visual summarization review of entities and their aspects. It describes experiments to identify comments about health and education aspects using co-occurrence where satisfactory results were obtained using the techniques Expected Mutual Information Measure and phi-squared. In which regards sentences polarization, experiments are performed with two classification approaches, one that classifies sentences in three classes and another that performs binary classifications in two stages.

An important issue for healthcare service providers is to achieve high levels of patient satisfaction. Collecting patient feedback about their experience in hospital enables providers to analyse their performance in terms of the levels of satisfaction and to identify the strengths and limitations of their service delivery. A common method of collecting patient feedback is via online portals and the forums of the service provider, where the patients can rate and comment about the service received. A challenge in analysing patient experience collected via online portals is that the amount of data can be huge and hence, prohibitive to analyse manually. In this thesis, an automated approach to patient experience analysis via Sentiment Analysis, Topic Modelling, and Dependency Parsing methods is presented. The patient experience data collected from the National Health Service (NHS) online portal in the United Kingdom is analysed in the study to understand this experience. The study was carried out in three iterations: (1) In the first, the Sentiment Analysis method was applied, which identified whether a given patient feedback item was positive or negative. (2) The second iteration involved applying Topic Modelling methods to identify automatically themes and topics from the patient feedback. Further, the outcomes of the Sentiment Analysis study from the first iteration were utilised to identify the patient sentiment regarding the topic being discussed in a given comment. (3) In the third iteration of the study, Dependency Parsing methods were employed for each patient feedback item and the topics identified. A method was devised to summarise the reason for a particular sentiment about each of the identified topics. The outcomes of the study demonstrate that text-mining methods can be effectively utilised to identify patients’ sentiment in their feedback as well as to identify the themes and topics discussed in it. The approach presented in the study was proven capable of effectively automatically analysing the NHS patient feedback database. Specifically, it can provide an overview of the positive and negative sentiment rate, identify the frequently discussed topics and summarise individual patient feedback items. Moreover, an API visualisation tool is introduced to make the outcomes more accessible to the health care providers.

With the rise of social media, people have gained a platform to express opinions and discuss current subjects with others. This thesis investigates whether a simple sentiment analysis — determining how positive a tweet about a given party is — can be used to predict the results of the Swedish general election and compares the results to betting odds and opinion polls. The results show that while the idea is an interesting one, and sometimes the data can point in the right direction, it is by far a reliable source to predict election outcomes.

In this thesis we explore the intensity of adjectives and how it can be predicted by different word features. We investigate how to accurately determine intensity between synonymous adjectives. For this, we look at features such as word frequency, number of senses and syllable length. Our study is inspired by life satisfaction and happiness surveys and the possibility that differences in intensity in the translation of the adjectives used for the questionnaires could explain the high degree of satisfaction that some countries show. We base our hypothesis on the theories of grammaticalization and semantic bleaching and the discoveries made by other researches about the relations between these word features and word intensity. We focus on studying Danish, English and French. Our study points to a statistically significant negative correlation between word frequency and word intensity.

Na WEB, são encontradas postagens sobre assuntos variados, notícias de celebridades, produtos e serviços. Tal conteúdo contém emoções positivas, negativas ou neutras. Minerar o sentimento da população sobre candidatos a eleições e seus aspectos em mídias virtuais pode ser realizado por meio de técnicas de Mineração de Opiniões. Existem soluções para fontes de opinião fortemente estruturadas, tais como revisões de produtos e serviços, no entanto o problema que se apresenta é realizar a mineração de opiniões em nível de aspecto em fontes de opiniões fracamente estruturadas. Além de avaliar conceitos relacionados à mineração de opiniões, o presente trabalho descreve a realização de um estudo de caso, o qual analisa fontes de opiniões fracamente estruturadas e propõe uma abordagem para minerar opiniões em nível de aspecto, utilizando como fontes de opinião comentários de leitores de jornais. O estudo de caso contribui (i) na concepção de uma abordagem para identificação da opinião em nível de aspecto sobre entidades eleitorais em comentários de notícias políticas, (ii) na aplicação de um método baseado em aprendizagem de máquina para classificar a opinião sobre entidades e seus aspectos em três classes (positivo, negativo e neutro), (iii) na representação da sumarização visual de opinião sobre entidades e seus aspectos. São descritos experimentos para identificar comentários que mencionam os aspectos saúde e educação, utilizando co-ocorrência, em que foram obtidos resultados satisfatórios utilizando as técnicas Expected Mutual Information Measure e phi-squared. Já para a polarização de sentenças, são realizados experimentos com duas abordagens de classificação: uma que classifica sentenças em três classes e outra que realiza classificações binárias em duas etapas. / In the WEB are found posts about various subjects like celebrity news, products and services. Such content has positive, negative or neutral emotions. Mining the population’s sentiments about elections candidates and their aspects in virtual media can be performed using Opinion Mining techniques. There are solutions for highly structured opinion sources, such as reviews of products and services, however the problem is how to perform aspect-based opinion mining in less structured opinions sources. Besides evaluating concepts related to opinion mining, this work describes a case study which analyzes weakly structured sources and proposes an approach to mine aspect-based opinions using as sources of sentiment reviews of newspaper readers. The case study contributes (i) designing an approach to identify the aspect-based opinion about electoral candidates in news political comments, (ii) to the application of a machine learning-based method to classify the opinion about entities and their aspects in three classes (positive, negative and neutral) (iii) to the representation of a visual summarization review of entities and their aspects. It describes experiments to identify comments about health and education aspects using co-occurrence where satisfactory results were obtained using the techniques Expected Mutual Information Measure and phi-squared. In which regards sentences polarization, experiments are performed with two classification approaches, one that classifies sentences in three classes and another that performs binary classifications in two stages.

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Business through e-commerce has become popular recently due to the massive amount of information available on internet. This has resulted in the abnormal number of reviews on websites like www.amazon.com  and www.ebay.com, where customers express their opinions about the purchases they have made. Analyzing customer’s behavior has become very important for the organizations to find new market trends and insights. For the potential customer  it becomes really difficult to get the knowledge about a product in the presence of such huge number of reviews and to sort the useful reviews and make good decision. The reviews available on these websites are in heterogeneous form i.e. structured  and unstructured form and needs to be stored in a consistent format. Since good decision requires quality information in limited amount of time, Yaakub et, al.(2011) have  proposed an ontology that uses a  multidimensional model to integrate customer’s characteristics and their comments about products. This approach first identifies the entities and then sentiments present in the customers reviews related to mobiles are transformed into an attribute table by using a 7 point polarity system (-3 to 3). The research proposed by Yaakub et, al.(2011) is in developing stage. The limitation of their approach is that the ontology proposed by them is too general. The authors have shown their desire that it should be tested for a large group of products. Also, Yaakub et, al.(2011) have used very short and simple comments for the manual extraction of features for which a sentiment has been expressed. Usually comments present on e-commerce websites are not that short and simple. In order to fulfill the aim of this thesis project, a case study has been conducted on websites www.amazon.com and www.ebay.com and the ontology proposed by  Yaakub et, al.(2011) has been refined for the three categories of mobile phones: smart phones, wet and dirty mobile phones and simple mobile phones. Further, sentiment analysis has been conducted by first using the ontology proposed by Yaakub et, al.(2011) and then by using the refined version of the ontologies for the three categories of mobile  in order to compare the results.

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.

Sentiment analysis concerns the computational study of opinions expressed in text. Social media domains provide a wealth of opinionated data, thus, creating a greater need for sentiment analysis. Typically, sentiment lexicons that capture term-sentiment association knowledge are commonly used to develop sentiment analysis systems. However, the nature of social media content calls for analysis methods and knowledge sources that are better able to adapt to changing vocabulary. Invariably existing sentiment lexicon knowledge cannot usefully handle social media vocabulary which is typically informal and changeable yet rich in sentiment. This, in turn, has implications on the analyser's ability to effectively capture the context therein and to interpret the sentiment polarity from the lexicons. In this thesis we use SentiWordNet, a popular sentiment-rich lexicon with a substantial vocabulary coverage and explore how to adapt it for social media sentiment analysis. Firstly, the thesis identifies a set of strategies to incorporate the effect of modifiers on sentiment-bearing terms (local context). These modifiers include: contextual valence shifters, non-lexical sentiment modifiers typical in social media and discourse structures. Secondly, the thesis introduces an approach in which a domain-specific lexicon is generated using a distant supervision method and integrated with a general-purpose lexicon, using a weighted strategy, to form a hybrid (domain-adapted) lexicon. This has the dual purpose of enriching term coverage of the general purpose lexicon with non-standard but sentiment-rich terms as well as adjusting sentiment semantics of terms. Here, we identified two term-sentiment association metrics based on Term Frequency and Inverse Document Frequency that are able to outperform the state-of-the-art Point-wise Mutual Information on social media data. As distant supervision may not be readily applicable on some social media domains, we explore the cross-domain transferability of a hybrid lexicon. Thirdly, we introduce an approach for improving distant-supervised sentiment classification with knowledge from local context analysis, domain-adapted (hybrid) and emotion lexicons. Finally, we conduct a comprehensive evaluation of all identified approaches using six sentiment-rich social media datasets.

Magíster en Ciencias, Mención COmputación / Los medios de comunicación social y en particular las plataformas de Microblogging se han consolidado como un espacio para el consumo y producción de información. Twitter se ha vuelto una de las plataforma más populares de este estilo y hoy en día tiene millones de usuarios que diariamente publican millones de mensajes personales o ``twiits''. Una parte importante de estos mensajes corresponden a opiniones personales, cuya riqueza y volumen ofrecen una gran oportunidad para el estudio de la opinión pública. Para tabajar con este alto volumen de opiniones digitales, se utilizan un conjunto de herramientas computacionales conocidas como métodos de análisis de sentimiento o minería de opinión. La utilidad de evaluar la opinión pública usando análisis de sentimiento sobre opiniones digitales genera controversia en la comunidad científica. Mientras diversos trabajos declaran que este enfoque permite capturar la opinión pública de una manera similar a medios tradicionales como las encuestas, otros trabajos declaran que este poder esta sobrevalorado. En este contexto, estudiamos el comportamiento estático y dinámico de las opiniones digitales para comprender su naturaleza y determinar las limitaciones de predecir su evolución en el tiempo. En una primera etapa se estudia el problema de identificar de manera automática los tuits que expresan una opinión, para luego inferir si es que esa opinión tiene una connotación positiva o negativa. Se propone una metodología para mejorar la clasificación de sentimiento en Twitter usando atributos basados en distintas dimensiones de sentimiento. Se combinan aspectos como la intensidad de opinión, la emoción y la polaridad, a partir de distintos métodos y recursos existentes para el análisis de sentimiento. La investigación muestra que la combinación de distintas dimensiones de opinión permite mejorar significativamente las tareas de clasificación de sentimientos en Twitter de detección de subjetividad y de polaridad. En la segunda parte del análisis se exploran las propiedades temporales de las opiniones en Twitter mediante el análisis de series temporales de opinión. La idea principal es determinar si es que las series temporales de opinión pueden ser usadas para crear modelos predictivos confiables. Se recuperan en el tiempo mensajes emitidos en Twitter asociados a un grupo definido de tópicos. Luego se calculan indicadores de opinión usando métodos de análisis de sentimiento para luego agregarlos en el tiempo y construir series temporales de opinión. El estudio se basa en modelos ARMA/ARIMA y GARCH para modelar la media y la volatilidad de las series. Se realiza un análisis profundo de las propiedades estadísticas de las series temporales encontrando que éstas presentan propiedades de estacionalidad y volatilidad. Como la volatilidad se relaciona con la incertidumbre, se postula que estas series no debiesen ser usadas para realizar pronósticos en el largo plazo. Los resultados experimentales obtenidos permiten concluir que las opiniones son objetos multidimensionales, donde las distintas dimensiones pueden complementarse para mejorar la clasificación de sentimiento. Por otro lado, podemos decir que las series temporales de opinión deben cumplir con ciertas propiedades estadísticas para poder realizar pronósticos confiables a partir de ellas. Dado que aún no hay suficiente evidencia para validar el supuesto poder predictivo de las opiniones digitales, nuestros resultados indican que una validación más rigurosa de los modelos estáticos y dinámicos que se constuyen a partir de estas opiniones permiten establecer de mejor manera los alcances de la minería de opinión.