Multilingual Twitter Sentiment Classification: The Role of Human Annotators

What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered

Bisociative knowledge discovery for microarray data analysis

Peer reviewe

SegMine workflows for semantic microarray data analysis in Orange4WS

Peer reviewe

Uncertainty, Decision Science, and Policy Making: A Manifesto for a Research Agenda

The financial crisis of 2008 was unforeseen partly because the academic theories that underpin policy making do not sufficiently account for uncertainty and complexity or learned and evolved human capabilities for managing them. Mainstream theories of decision making tend to be strongly normative and based on wishfully unrealistic “idealized” modeling. In order to develop theories of actual decision making under uncertainty, we need new methodologies that account for how human (sentient) actors often manage uncertain situations “well enough.” Some possibly helpful methodologies, drawing on digital science, focus on the role of emotions in determining people’s choices; others examine how people construct narratives that enable them to act; still others combine qualitative with quantitative data

Model-Based Diagnosis: An Overview

Diagnosis is an important application area of Artificial Intelligence. First generation expert diagnostic systems had exhibited difficulties which motivated the development of model-based reasoning techniques. Model-based diagnosis is the activity of locating malfunctioning components of a system solely on the basis of its structure and behavior. The paper gives a brief overview of the main concepts, problems, and research results in this area. 1 Introduction Diagnosis is one of the earliest areas in which application of Artificial Intelligence techniques was attempted. The diagnosis of a system which behaves abnormally consists of locating those subsystems whose abnormal behavior accounts for the observed behavior. For example, a system being diagnosed might be a mechanical device exhibiting malfunction, or a human patient. There are two fundamentally different approaches to diagnostic reasoning. In the first, heuristic approach, one attempts to codify diagnostic rules of thumb and p..

Hierarchical Model-Based Diagnosis

Model-based reasoning about a system requires an explicit representation of the system's components and their connections. Diagnosing such a system consists of locating those components whose abnormal behavior accounts for the faulty system behavior. In order to increase the efficiency of model-based diagnosis, we propose a model representation at several levels of detail, and define three refinement (abstraction) operators. We specify formal conditions that have to be satisfied by the hierarchical representation, and emphasize that the multi-level scheme is independent of any particular single-level model representation. The hierarchical diagnostic algorithm which we define turns out to be very general. We show that it emulates the bisection method, and can be used for hierarchical constraint satisfaction. We apply the hierarchical modeling principle and diagnostic algorithm to a medium-scale medical problem. The performance of a four-level qualitative model of the heart is compared t..