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Sentiment Analysis is the task of extracting and classifying opinionated content in natural language texts. Common subtasks are the distinction between opinionated and factual texts, the classification of polarity in opinionated texts, and the extraction of the participating entities of an opinion(-event), i.e. the source from which an opinion emanates and the target towards which it is directed. With the emerging Web 2.0 which describes the shift towards a highly user-interactive communication medium, the amount of subjective content on the World Wide Web is steadily increasing. Thus, there is a growing need for automatically processing this type of content which is provided by sentiment analysis. Both natural language processing, which is the task of providing computational methods for the analysis and representation of natural language, and machine learning, which is the task of building task-specific classification models on the basis of empirical data, may be instrumental in mastering the challenges of the automatic sentiment analysis of written text. Many problems in sentiment analysis have been proposed to be solved with machine learning methods exclusively using a fairly low-level feature design, such as bag of words, containing little linguistic information. In this thesis, we examine the effectiveness of linguistic features in various subtasks of sentiment analysis. Thus, we heavily draw from the insights gained by natural language processing. The application of linguistic features can be applied on various classification methods, be it in rule-based classification, where the linguistic features are directly encoded as a classifier, in supervised machine learning, where these features complement basic low-level features, or in bootstrapping methods, where these features form a rule-based classifier generating a labeled training set from which a supervised classifier can be trained. In this thesis, we will in particular focus on scenarios where the combination of linguistic features and machine learning methods is effective. We will look at common text classification tasks, both coarse-grained and fine-grained, and extraction tasks.
Die vorliegende Arbeit geht der Frage nach, wie bzw. mit welchen sprachlichen Mitteln der Islam im öffentlichen Diskurs konstituiert wird. Hierfür wurde ein Korpus aus überregionalen Medientexten erstellt und qualitativ analysiert. Die Auswertung des gesamten Korpus weist darauf hin, welche inhaltlichen Merkmale im Islamdiskurs sprachlich nachweisbar sind und sich im gesamten Diskurs stets wiederholen. Schlüsselwörter wie Islam, Islamismus, Islamisierung, Muslim, Dschihad, Scharia oder Koran wurden detailliert präsentiert. Außerdem wurden die aus dem untersuchten Korpus entstandenen Stereotype rekonstruiert. Weiterhin wurden Metaphern bzw. Metaphernkonzepte untersucht, die sich im Islamdiskurs abbilden lassen. Exemplarisch anhand der drei Weltereignisse Iranische Revolution 1978/79, 11. September 2001 und Arabischer Frühling 2011 hat die vorliegende Arbeit gezeigt, wie der Islam in unterschiedlichen Zeitabständen wahrgenommen wird und inwieweit gesellschaftspolitische Ereignisse und Auseinandersetzungen die Thematisierung des Islams beeinflussen können.