Theses

Here you can find theses of Know-Center employees

2016

Steinbauer Florian

German Sentiment Analysis on Facebook Posts i

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Social media monitoring has become an important means for business analytics and trend detection, comparing companies with each other or keeping a healthy customer relationship. While English sentiment analysis is very closely researched, not much work has been done on German data analysis. In this work we will (i) annotate ~700 posts from 15 corporate Facebook pages, (ii) evaluate existing approaches capable of processing German data against the annotated data set and (iii) due to the insufficient results train a two-step hierarchical classifier capable of predicting posts with an accuracy of 70%. The first binary classifier decides whether the post is opinionated. If the outcome is not neutral, the second classifier predicts the polarity of the document. Further we will apply the algorithm in two application scenarios where German Facebook posts, in particular the fashion trade chain Peek&Cloppenburg and the Austrian railway operators OeBB and Westbahn will be analyzed.
2016

Fraz Koini Josef

Study on Health Trackers i

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The rising distribution of compact devices with numerous sensors in the last decade has led to an increasing popularity of tracking fitness and health data and storing those data sets in apps and cloud environments for further evaluation. However, this massive collection of data is becoming more and more interesting for companies to reduce costs and increase productivity. All this possibly leads to problematic impacts on people’s privacy in the future. Hence, the main research question of this bachelor’s thesis is: “To what extent are people aware of the processing and pro- tection of their personal health data concerning the utilisation of various health tracking solutions?” This thesis investigates the historical development of personal fitness and health tracking, gives an overview of current options for users and presents potential problems and possible solutions regarding the use of health track- ing technology. Furthermore, it outlines the societal impact and legal issues. The results of a conducted online survey concerning the distribution and usage of health tracking solutions as well as the participants’ views on privacy concerning data sharing with service and insurance providers, ad- vertisers and employers are presented. Given those results, the necessity and importance of data protection according to the fierce opposition of the participants to various data sharing scenarios is expressed.
2016

Suschnigg Josef

Mobile Unterstützung zur Reflexion der Übungspraxis bei Musikstudierenden i

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Es wird eine mobile Anwendung entwickelt, die Musikstudierende dabei unterstützt reflexiv ein Instrument zu lernen. Der Anwender soll in der Lage sein seinen Übungserfolg über Selbstbeobachtung festzustellen, um in weiterer Folge Übungsstrategien zu finden, die die Übungspraxis optimieren soll. Kurzfristig stellt die Anwendung dem Benutzer für verschiedene Handlungsphasen einer Übungseinheit (preaktional, aktional und postaktional) Benutzeroberflächen zur Verfügung. Mit Hilfe von Leitfragen, oder vom Anwender formulierten Fragen, wird das Üben organisiert, strukturiert bzw. selbstreflektiert und evaluiert. Im Optimalfall kann der Anwender seinen Lernprozess auch auf Basis von Tonaufnahmen mitverfolgen. Langfristig können alle Benutzereingaben wieder abgerufen werden. Diese werden journalartig dargestellt und können zur Selbstreflexion oder auch gemeinsam mit einer Lehrperson ausgewertet werden.

2016

Ivantstits Matthias

Quantitative & qualitative Market-Analysis i

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The buzzword big data is ubiquitous and has much impact on our everyday live and many businesses. Since the outset of the financial market, it is the aim to find some explanatory factors which contribute to the development of stock prices, therefore big data is a chance to do so. Gathering a vast amount of data concerning the financial market, filtering and analysing it, is of course tightly tied to predicting future stock prices. A lot of work has already been done with noticeable outcomes in this field of research. However, the question was raised, whether it is possible to build a tool with a large quantity of companies and news indexed and a natural language processing tool suitable for everyday applications. The sentiment analysis tool that was utilised in the development of this implementation is sensium.io. To achieve this goal two main modules were built. The first is responsible for constructing a filtered company index and for gathering detailed information about them, for example news, balance sheet figures and stock prices. The second is accountable for preprocessing the collected data and analysing them. This includes filtering unwanted news, translating them, calculating the text polarity and predicting the price development based on these facts. Utilising all these modules, the optimal period for buying and selling shares was found to be three days. This means buying some shares on the day of the news publication and selling them three days later. Pursuant to this analysis expected return is 0.07 percent a day, which might not seem much, however this would result in an annualised performance of 30.18 percent. This idea can also be outlaid in the contrary direction, telling the user when to sell his shares. Which could help an investor to find the ideal time to sell his company shares.
2016

Toller Maximilian

Automated Season Length Detection in Time Series i

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The in-depth analysis of time series has been a central topic of research in the last years. Many of the present methods for finding periodic patterns and features require the use to input the time series’ season length. Today, there exist a few algorithms for automated season length approximation, yet many of them rely on simplifications such as data discretization. This thesis aims to develop an algorithm for season length detection that is more reliable than existing methods. The process developed in this thesis estimates a time series’ season length by interpolating, filtering and detrending the data and then analyzing the distances between zeros in the directly corresponding autocorrelation function. This method was tested against the only comparable open source algorithm and outperformed it by passing 94 out of 125 tests, while the existing algorithm only passed 62 tests. The results do not necessarily suggest a superiority of the new autocorrelation based method, but rather a supremacy of the new implementation. Further related studies might assess and compare the value of the theoretical concept.
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