The aim of this study is to analyze tweets on Twitter to the topic Islamic State regarding their positive and negative emotions by performing a sentiment analysis. People of different regions and cultures have a specific emotionality concerning the IS, not only in daily life but also in writing microblogs. With the help of sentiment analysis, the following question should be answered: "What are Twitter user's opinions on ISIS in different states worldwide?" For this purpose, a Python tool is developed that interacts with the Twitter Streaming API to retrieve Tweets that are IS-related, saving them with associated countries. Close to 500,000 tweets are collected by this tool over a period of nearly six weeks. Sentiment analysis of Tweets is made with a tool invented by Janina Nikolic. Results are normalized with additional, self-developed Python scripts and analyzed with Microsoft Excel and IBM SPSS. The results show that most of the Tweets in the countries have a negative attitude towards the Islamic State, and only a very limited set of states has a neutral or positive total sentiment in the results of this study. Sentiment is influenced by various factors, including political systems, geographic location and distance towards the area where the Islamic State is active and terroristic attacks.

This article analyses the effect of anaphora resolution on information retrieval performance for systems with relevance ranking. It will be investigated if the Mean Average Precision of a retrieval system is improved after an intellectual replacement of all anaphors in a corpus with various texts. These texts mostly consist of news stories and fairy tales, thus covering two varying genres with different amounts of anaphors. A model retrieval system is developed using Lucene to measure the effects of anaphora resolution. Different queries are used and the rankings are analysed in order to show the changes induced by the anaphora resolution. In addition, approaches of automated anaphora resolution are considered. It turns out that the Mean Average Precision improves noticeably by 36% after the anaphora resolution. Thus, it is highly recommended to improve existing approaches of automated anaphora resolution in the future as current attempts do not yet yield satisfying results.

No abstract