Open Access

In this paper, we describe MLSA, a publicly available multi-layered reference corpus for German-language sentiment analysis. The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity. The sentence-layer annotation, as the most coarse-grained annotation, focuses on aspects of objectivity, subjectivity and the overall polarity of the respective sentences. Layer 2 is concerned with polarity on the word- and phrase-level, annotating both subjective and factual language. The annotations on Layer 3 focus on the expression-level, denoting frames of private states such as objective and direct speech events. These three layers and their respective annotations are intended to be fully independent of each other. At the same time, exploring for and discovering interactions that may exist between different layers should also be possible. The reliability of the respective annotations was assessed using the average pairwise agreement and Fleiss’ multi-rater measures. We believe that MLSA is a beneficial resource for sentiment analysis research, algorithms and applications that focus on the German language.

Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level differs substantially from document-level classification in that resulting bag-of-words feature vectors tend to be very sparse resulting in a lower classification accuracy. In this paper, we show that performance can be improved by adding features specifically designed for sentence-level polarity classification. We consider both explicit polarity information and various linguistic features. A great proportion of the improvement that can be obtained by using polarity information can also be achieved by using a set of simple domain-independent linguistic features.

In this paper, we explore the effectiveness of bootstrapping supervised machine-learning polarity classifiers using the output of domain-independent rule-based classifiers. The benefit of this method is that no labeled training data are required. Still, this method allows to capture in-domain knowledge by training the supervised classifier on in-domain features, such as bag of words. We investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. The former addresses the issue in how far relevant constructions for polarity classification, such as word sense disambiguation, negation modeling, or intensification, are important for this self-training approach. We not only compare how this method relates to conventional semi-supervised learning but also examine how it performs under more difficult settings in which classes are not balanced and mixed reviews are included in the dataset.

This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.

Der Beitrag beschäftigt sich mit der Interaktion zwischen blinden und sehenden Personen bei der kooperativen Anfertigung einer Audiodeskription. Eine Audio-deskription ist die verbale Beschreibung visueller Inhalte für Sehbeeinträchtigte und stellt eine Sonderform der Translation dar. Auf der Basis von Videodaten wird die Kooperation eines Dreierteams mit den Verfahren der multimodalen Interaktionsanalyse untersucht. Ein Charakteristikum dieser Kooperation besteht darin, dass eines der Teammitglieder blind ist und die beiden anderen sehen können. Das Erkenntnisinteresse richtet sich besonders auf die professionelle Beteiligung des blinden Teammitglieds an der Interaktion. Die Analyse zeigt, wie Blindheit als Ressource für die kooperative Herstellung der Audiodeskription genutzt wird und wie die Beteiligten in einer visuell asymmetrischen Situation interagieren. Der Beitrag ist eine der seltenen Untersuchungen, die sich mit professioneller Interaktion zwischen Blinden und Sehenden beschäftigen. Er diskutiert Aspekte von genereller Relevanz für die weitere Entwicklung der empirischen Interaktionsforschung, vor allem in Bezug auf eine Erweiterung von Beteiligungsperspektiven in Richtung Inklusion.