Description: This paper discusses multilingual subjectivity analysis using machine translation. Although research in other languages is increasing, much of the work in subjectivity analysis has been applied to English data, mainly due to the large body of electronic resources and tools that are available for this language. In this paper, the authors propose and evaluate methods that can be employed to transfer a repository of subjectivity resources across languages. Specifically, the authors attempt to leverage on the resources available for English and, by employing machine translation, generate resources for subjectivity analysis in other languages. Through comparative evaluations on two different languages (Romanian and Spanish), the authors show that automatic translation is a viable alternative for the construction of resources and tools for subjectivity analysis in a new target language.

Description: This paper discusses integrating knowledge for subjectivity sense labeling. Abstract: This paper introduces an integrative approach to automatic word sense subjectivity annotation. We use features that exploit the hierarchical structure and domain information in lexical resources such as WordNet, as well as other types of features that measure the similarity of glosses and the overlap among sets of semantically related words. Integrated in a machine learning framework, the entire set of features is found to give better results than any individual type of feature.