Prediktion av Svenska Nyhetsartiklars Populäritet

In this work, 132,229 articles from a Swedish news publisher are used to explore news article popularity prediction. Linear-, k-Nearest Neighbor- and Support Vector Regression are evaluated using the two different metrics root mean squared error and R2. The problem is then relaxed into only attempting to rank the articles relative to each other. The prediction problem is also explored as a classification problem using the classes Low, Mid and High popularity. The classifiers evaluated are Naive Bayes and SVM using pre-defined features and using a Bag-of-words feature set. The results were analyzed to understand what information they can bring to the editors at the publisher and news agencies in general. The results clearly showed that the manually set metadata newsvalue had a large impact on article performance. A survey was done with editors to compare human prediction performance with the classifier performance. Although the SVM classifier performs with higher accuracy than the editors (59% vs 55%) the models are considered weak in their current state

Prediktion av Svenska Nyhetsartiklars Populäritet

In this work, 132,229 articles from a Swedish news publisher are used to explore news article popularity prediction. Linear-, k-Nearest Neighbor- and Support Vector Regression are evaluated using the two different metrics root mean squared error and R2. The problem is then relaxed into only attempting to rank the articles relative to each other. The prediction problem is also explored as a classification problem using the classes Low, Mid and High popularity. The classifiers evaluated are Naive Bayes and SVM using pre-defined features and using a Bag-of-words feature set. The results were analyzed to understand what information they can bring to the editors at the publisher and news agencies in general. The results clearly showed that the manually set metadata newsvalue had a large impact on article performance. A survey was done with editors to compare human prediction performance with the classifier performance. Although the SVM classifier performs with higher accuracy than the editors (59% vs 55%) the models are considered weak in their current state

Prediktion av Svenska Nyhetsartiklars Populäritet

In this work, 132,229 articles from a Swedish news publisher are used to explore news article popularity prediction. Linear-, k-Nearest Neighbor- and Support Vector Regression are evaluated using the two different metrics root mean squared error and R2. The problem is then relaxed into only attempting to rank the articles relative to each other. The prediction problem is also explored as a classification problem using the classes Low, Mid and High popularity. The classifiers evaluated are Naive Bayes and SVM using pre-defined features and using a Bag-of-words feature set. The results were analyzed to understand what information they can bring to the editors at the publisher and news agencies in general. The results clearly showed that the manually set metadata newsvalue had a large impact on article performance. A survey was done with editors to compare human prediction performance with the classifier performance. Although the SVM classifier performs with higher accuracy than the editors (59% vs 55%) the models are considered weak in their current state

Development in Augmented Reality with Unity

Syftet med den här rapporten är att behandla frågeställningar utifrån projektet, vars mål är att utveckla ett Augmented Reality-ramverk för att skapa värde för en kund, BioOptico. Punkter som programmeringsmiljön Unitys lämplighet vid utveckling av mobila AR-applikationer, hur BioVision kan implementeras så att det ger värde för BioOptico, vilka erfarenheter som kan dokumenteras efter arbetet med projektet och vilket stöd SEMAT Kernel ALPHA ger för det här projektet behandlas i den här rapporten. För att besvara dessa frågor används verktygen Unity och SEMAT Kernel ALPHA state cards under utvecklingen av BioVision. Genom agil utveckling och framtagning av tidiga prototyper undersöks vilka implementationssätt som ger värde för BioOptico och vilken dokumentation av erfarenheter som kan vara intressant för framtida projekt. Som resultat dokumenteras de områden där Unity lämpar sig väl för utveckling av mobila AR-applikationer, vilka de viktigaste beståndsdelarna var för hur BioVision implementerades så att det ger värde för BioOptico och vilka erfarenheter och vilket stöd som ges av utveckling av BioVision respektive användning av SEMAT Kernel ALPHA:s. Som slutsats ses Unity som en lämplig utvecklingsmiljö för utveckling av AR-applikationer, medan SEMAT Kernel ALPHA kan vara användbart, dock överflödigt, vid utveckling av BioVision. De viktigaste lärdomarna att ta med sig från projektet är att planera, utveckla och dokumentera i god tid. Slutligen dras slutsatsen om att strukturen på projektet och inte bara produkten som skapas spelar en stor roll för hur applikationen kan ge värde för BioOptico