Debeyem DennisEder, TimGuigas, Paul VincentSchuberth, ViktoriaBecker, Michael2019-10-142019-10-142019978-3-88579-449-3https://dl.gi.de/handle/20.500.12116/28995The sentiment analysis of social media data increasingly gains importance in business and research. But still, topical algorithms cope with problems, since it is reasonably manageable to extract the tonality of a social media post, but not the authors attitude towards a given topic. However, in most cases, this is the relevant information users of social media analysis tools are looking for. To tackle this problem, we propose a context-based algorithm that not only focuses on isolated postings, but also takes the authorsŠ earlier postings and their interactions with other usersŠ posts into account to derive their actual opinion on a subject. To evaluate this approach, we implemented a test system and compared the algorithmŠs results to manually assessed sentiments.ensentiment analysissocial media analysisopinion miningText/Conference Paper1614-3213