Dynamic Sentiment Analysis for Measuring Media Bias

Abstract

Analyzing the sentiments of texts in the field of social news and news media is a big area of interest for many researchers around the world. It is a well-known problem to “teach” machines to understand the sentiments of texts e.g. news media. This master thesis aims to unveil the sentiments towards persons of public interests, who are often presented in emotionally charged contexts, for different media and over time with a focus on Vienna. Although sentiment analysis has been widely applied for analysing news and social media content, there are still many challenges unsolved. This is particularly true for the area of news media as it is not as well researched in this context as the area of social media (e.g., Twitter). Sentiment analysis is strongly language dependent. Sentiment analysis of German texts, however, hardly considers the specifics of Austrian German. To tackle these research gaps, this research performs a supervised machine learning (SML) analysis for analyzing the sentiment towards politicians over the time, and different media of the Austrian Media Corpus (AMC) different methods were compared. Sentiment analysis has been shown to be very challenging in this narrow domain. Nevertheless, results show that it is possible to predict the polarity of politicians over time. Modern state-of-the-art approaches such as BERT based models outperform traditional approaches but are not transparent, which is important when it comes to explainability and fairness. To overcome this lack of transparency, a lexical-based method was used, resulting in a new sentiment dictionary. This sentiment dictionary can be further used for research in this field and is called “Austrian Language Polarity in Newspapers (ALPIN)”. The developed models form the basis of a web application to explore media coverage of Viennese politicians and related sentiment dynamics.