Emotion and polarity prediction from Twitter

Classification of public information from microblogging and social networking services could yield interesting outcomes and insights into the social and public opinions towards different services, products, and events. Microblogging and social networking data are one of the most helpful and proper indicators of public opinion. The aim of this paper is to classify tweets to their classes using cross validation and partitioning the data across cities using supervised machine learning algorithms. Such an approach was used to collect real time Twitter microblogging data tweets towards mentioning iPad and iPhone in different locations in order to analyse and classify data in terms of polarity: positive or negative, and emotion: anger, joy, sadness, disgust, fear, and surprise. We have collected over eighty thousand tweets that have been pre-processed to generate document level ground-truth and labelled according to Emotion and Polarity. We also compared some approaches in order to measures the performance of K-NN, Nave Bayes, and SVM classifiers. We found that the K-NN, Nave Bayes, SVM, and ZeroR have a reasonable accuracy rates, however, the K-NN has outperformed the Nave Bayes, SVM, and ZeroR based on the achieved accuracy rates and trained model time. The K-NN has achieved the highest accuracy rates 96.58% and 99.94% for the iPad and iPhone emotion data sets using cross validation technique respectively. Regarding partitioning the data per city, the K-NN has achieved the highest accuracy rates 98.8% and 99.95% for the iPad and iPhone emotion data sets respectively. Regarding the polarity data sets using both cross validation and partitioning data per city, the K-NN achieved 100% for the all polarity datasets

Einflussfaktoren für erfolgreiche Datenmigrationen im Rahmen von IT-Projekten.

Mit dem Unternehmenswachstum altern auch die Systeme, welche nach einer bestimmten Anzahl an Jahren das Ende ihrer Lebensphase erreichen und nicht mehr verwaltet werden können. Dieser Grund ist einer von vielen, weshalb Systeme in Unternehmen abgelöst und durch neue ersetzt werden. In den meisten Fällen werden Daten aus dem Altsystem in das neue System übernommen. Es findet sich zunehmend Literatur zum Thema Datenqualität in Zusammenhang mit Datenqualitätsmanagement oder Data-Warehouse-Systemen. Deshalb untersucht die vorliegende Bachelorarbeit den Einfluss hoher Datenqualität und effektive Migrationsplanungen für den Erfolg der IT-Projekte. Obwohl die Datenmigration ein bekanntes Verfahren ist, scheitert die Mehrheit aller Datenmigrationsprojekte. Es wird daher davon ausgegangen, dass die Ursache des Problems auf die Unterschätzung der Faktoren Datenqualität beruht und die Migrationsplanung unzureichend definiert wird. Daher ermittelt diese Bachelorarbeit die Antwort auf die Frage, welchen Einfluss eine hohe Datenqualität und effektive Migrationsplanung auf den IT-Projekterfolg hat

Workshop, Long and Short Paper, and Poster Proceedings from the Fourth Immersive Learning Research Network Conference (iLRN 2018 Montana), 2018.

ILRN 2018 - Conferência internacional realizada em Montana de 24-29 de june de 2018.Workshop, short paper, and long paper proceedingsinfo:eu-repo/semantics/publishedVersio

Too hot to handle: the global impact of extreme heat

Heatwaves are the deadliest weather hazard. Extreme heat also impacts cross-sections of society, from health to agriculture to infrastructure. The World Meteorological Organisation and the World Health Organisation recommend that countries should implement early warning systems to reduce the impacts of extreme heat. Despite this mandate no global heat hazard alert system currently exists. In addition, heatwave impacts are often under-reported by meteorological organisation databases and reports and in the English news media. This leads to them sometimes being known as silent or invisible killers. An interdisciplinary approach is taken in this thesis to investigate the impact of extreme heat and policy measures and to explore the development of a global heat hazard early warning system. This is presented through a systems approach framed using an adapted version of the WHO framework Operational framework for building climate resilient health systems. There are 4 components addressing different aspects of the system each with an objective and these are: 1. Mobilization and governance: assess policy prioritization and governance,2. Health Information Systems: evaluate the trends and modelling for extreme heat, 3. Essential Technologies: develop new technologies to reduce risk to heat and 4. Service Delivery: consider the communication of heat risks and impacts within wider culture. Overall, the research presented in this thesis provides research for a global heat hazard alert system focused on health impacts. An open-source python library for thermal comfort called thermofeel is developed and evaluated. In addition, the way in which extreme heat and heatwaves are communicated in English language research, policy and news media is explored, to start assessing what the best way to communicate heat stress risk might be on a global scale. All of this raises the profile of heatwave risk to ensure their impacts becomes more visible