Timm Oliver Sprenger

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Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text(More)
This study investigates whether microblogging messages on Twitter validly mirror the political landscape off-line and can be used to predict election results. In the context of the 2009 German federal election, we conducted a sentiment analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that(More)
In the context of a national election, this study explores more than 69,000 Twitter messages containing mentions of political parties and about 2,500 related user profiles to investigate the network structure of political microbloggers with respect to, first, their party preference and, second, the topics they discuss. We find that political microbloggers(More)
In their comment, Jungherr, Jürgens, and Schoen (2010) challenged part of the results presented in our original article (Tumasjan, Sprenger, Sandner, & Welpe, 2010). The present response addresses their points of concern and demonstrates that the conclusions drawn in Tumasjan et al. (2010) are well supported by both data and analyses.
Delineating industry groups of related firms and identifying strategic peers is important for both financial practitioners and scholars. Our study explores whether the degree to which pairs of companies are associated with each other in an online stock forum is related to the comovement of their stocks. We find that our news-based measure of relatedness can(More)
This paper presents a survey of sentiments analysis for product review. Online social and news media has become a very popular for users to share their opinions and generate prosperous and timely information about real world events of all kinds. Several efforts were dedicated for mining opinions and sentiments automatically from natural language in social(More)
Do you have a lot of unstructured data in image files? Are you interested in finding out the sentiment of those files? If you are SENTIEXTRACT is the perfect tool for you. In this paper, we have given an insight of our system (SENTIEXTRACT). Our system works on algorithms such as tesseract-ocr to convert image files to text files and naïve bayes classifier(More)
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