2,835 research outputs found
HiER 2015. Proceedings des 9. Hildesheimer Evaluierungs- und Retrievalworkshop
Die Digitalisierung formt unsere Informationsumwelten. Disruptive Technologien dringen verstärkt und immer schneller in unseren Alltag ein und verändern unser Informations- und Kommunikationsverhalten. Informationsmärkte wandeln sich. Der 9. Hildesheimer Evaluierungs- und Retrievalworkshop HIER 2015 thematisiert die Gestaltung und Evaluierung von Informationssystemen vor dem Hintergrund der sich beschleunigenden Digitalisierung. Im Fokus stehen die folgenden Themen: Digital Humanities, Internetsuche und Online Marketing, Information Seeking und nutzerzentrierte Entwicklung, E-Learning
Sentiment Analysis: State of the Art
We present the state of art in sentiment analysis which covers the purpose of sentiment analysis, levels of sentiment analysis and processes that could be used to measure polarity and classify labels. Moreover, brief details about some resources of sentiment analysis are included
Trends and Topics: Characterizing Echo Chambers' Topological Stability and In-group Attitudes
Social Network sites are fertile ground for several polluting phenomena
affecting online and offline spaces. Among these phenomena are included echo
chambers, closed systems in which the opinions expressed by the people inside
are exacerbated for the effect of the repetition, while opposite views are
actively excluded. This paper offers a framework to explore, in a
platform-independent manner, the topological changes through time of echo
chambers, while considering the content posted by users and the attitude
conveyed in discussing specific controversial issues.
The proposed framework consists of four steps: (i) data collection and
annotation of users' ideology regarding a controversial topic, (ii)
construction of a dynamic network of interactions, (iii) ECs extraction and
analysis of their dynamics, and (iv) topic extraction and valence analysis. The
paper then enhances the formalization of the framework by conducting a case
study on Reddit threads about sociopolitical issues (gun control, American
politics, and minorities discrimination) during the first two years and a half
of Donald Trump's presidency.
The results unveil that users often stay inside echo chambers over time.
Furthermore, in the analyzed discussions, the focus is on controversies related
to right-wing parties and specific events in American and Canadian politics.
The analysis of the attitude conveyed in the discussions shows a slight
inclination toward a more negative or neutral attitude when discussing
particularly sensitive issues, such as fascism, school shootings, or police
violence.Comment: 24 pages, including 8 pages of Supplementary materials. Submitted to
IEEE Acces
Advances in Emotion Recognition: Link to Depressive Disorder
Emotion recognition enables real-time analysis, tagging, and inference of cognitive affective states from human facial expression, speech and tone, body posture and physiological signal, as well as social text on social network platform. Recognition of emotion pattern based on explicit and implicit features extracted through wearable and other devices could be decoded through computational modeling. Meanwhile, emotion recognition and computation are critical to detection and diagnosis of potential patients of mood disorder. The chapter aims to summarize the main findings in the area of affective recognition and its applications in major depressive disorder (MDD), which have made rapid progress in the last decade
Argumentation dialogues in web-based GDSS: an approach using machine learning techniques
Tese de doutoramento em InformaticsA tomada de decisão está presente no dia a dia de qualquer pessoa, mesmo que muitas vezes ela
não tenha consciência disso. As decisões podem estar relacionadas com problemas quotidianos, ou
podem estar relacionadas com questões mais complexas, como é o caso das questões organizacionais.
Normalmente, no contexto organizacional, as decisões são tomadas em grupo.
Os Sistemas de Apoio à Decisão em Grupo têm sido estudados ao longo das últimas décadas com o
objetivo de melhorar o apoio prestado aos decisores nas mais diversas situações e/ou problemas a resolver.
Existem duas abordagens principais à implementação de Sistemas de Apoio à Decisão em Grupo:
a abordagem clássica, baseada na agregação matemática das preferências dos diferentes elementos do
grupo e as abordagens baseadas na negociação automática (e.g. Teoria dos Jogos, Argumentação, entre
outras).
Os atuais Sistemas de Apoio à Decisão em Grupo baseados em argumentação podem gerar uma
enorme quantidade de dados. O objetivo deste trabalho de investigação é estudar e desenvolver modelos
utilizando técnicas de aprendizagem automática para extrair conhecimento dos diálogos argumentativos
realizados pelos decisores, mais concretamente, pretende-se criar modelos para analisar, classificar e
processar esses dados, potencializando a geração de novo conhecimento que será utilizado tanto por
agentes inteligentes, como por decisiores reais. Promovendo desta forma a obtenção de consenso entre
os membros do grupo. Com base no estudo da literatura e nos desafios em aberto neste domínio,
formulou-se a seguinte hipótese de investigação - É possível usar técnicas de aprendizagem automática
para apoiar diálogos argumentativos em Sistemas de Apoio à Decisão em Grupo baseados na web.
No âmbito dos trabalhos desenvolvidos, foram aplicados algoritmos de classificação supervisionados
a um conjunto de dados contendo argumentos extraídos de debates online, criando um classificador
de frases argumentativas que pode classificar automaticamente (A favor/Contra) frases argumentativas
trocadas no contexto da tomada de decisão. Foi desenvolvido um modelo de clustering dinâmico para
organizar as conversas com base nos argumentos utilizados. Além disso, foi proposto um Sistema de
Apoio à Decisão em Grupo baseado na web que possibilita apoiar grupos de decisores independentemente
de sua localização geográfica. O sistema permite a criação de problemas multicritério e a configuração
das preferências, intenções e interesses de cada decisor. Este sistema de apoio à decisão baseado na
web inclui os dashboards de relatórios inteligentes que são gerados através dos resultados dos trabalhos
alcançados pelos modelos anteriores já referidos. A concretização de cada um dos objetivos permitiu
validar as questões de investigação identificadas e assim responder positivamente à hipótese definida.Decision-making is present in anyone’s daily life, even if they are often unaware of it. Decisions can be
related to everyday problems, or they can be related to more complex issues, such as organizational
issues. Normally, in the organizational context, decisions are made in groups.
Group Decision Support Systems have been studied over the past decades with the aim of improving
the support provided to decision-makers in the most diverse situations and/or problems to be solved.
There are two main approaches to implementing Group Decision Support Systems: the classical approach,
based on the mathematical aggregation of the preferences of the different elements of the group, and the
approaches based on automatic negotiation (e.g. Game Theory, Argumentation, among others).
Current argumentation-based Group Decision Support Systems can generate an enormous amount
of data. The objective of this research work is to study and develop models using automatic learning techniques
to extract knowledge from argumentative dialogues carried out by decision-makers, more specifically,
it is intended to create models to analyze, classify and process these data, enhancing the generation
of new knowledge that will be used both by intelligent agents and by real decision-makers. Promoting in
this way the achievement of consensus among the members of the group. Based on the literature study
and the open challenges in this domain, the following research hypothesis was formulated - It is possible
to use machine learning techniques to support argumentative dialogues in web-based Group Decision
Support Systems.
As part of the work developed, supervised classification algorithms were applied to a data set containing
arguments extracted from online debates, creating an argumentative sentence classifier that can
automatically classify (For/Against) argumentative sentences exchanged in the context of decision-making.
A dynamic clustering model was developed to organize conversations based on the arguments used. In
addition, a web-based Group Decision Support System was proposed that makes it possible to support
groups of decision-makers regardless of their geographic location. The system allows the creation of multicriteria
problems and the configuration of preferences, intentions, and interests of each decision-maker.
This web-based decision support system includes dashboards of intelligent reports that are generated
through the results of the work achieved by the previous models already mentioned. The achievement of
each objective allowed validation of the identified research questions and thus responded positively to the
defined hypothesis.I also thank to Fundação para a Ciência e a Tecnologia, for the Ph.D. grant funding with the reference: SFRH/BD/137150/2018
Challenges and opportunities beyond structured data in analysis of electronic health records
Electronic health records (EHR) contain a lot of valuable information about individual patients and the whole population. Besides structured data, unstructured data in EHRs can provide extra, valuable information but the analytics processes are complex, time-consuming, and often require excessive manual effort. Among unstructured data, clinical text and images are the two most popular and important sources of information. Advanced statistical algorithms in natural language processing, machine learning, deep learning, and radiomics have increasingly been used for analyzing clinical text and images. Although there exist many challenges that have not been fully addressed, which can hinder the use of unstructured data, there are clear opportunities for well-designed diagnosis and decision support tools that efficiently incorporate both structured and unstructured data for extracting useful information and provide better outcomes. However, access to clinical data is still very restricted due to data sensitivity and ethical issues. Data quality is also an important challenge in which methods for improving data completeness, conformity and plausibility are needed. Further, generalizing and explaining the result of machine learning models are important problems for healthcare, and these are open challenges. A possible solution to improve data quality and accessibility of unstructured data is developing machine learning methods that can generate clinically relevant synthetic data, and accelerating further research on privacy preserving techniques such as deidentification and pseudonymization of clinical text
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