This paper introduces a method for creating a subjectivity lexicon for languages with scarce resources. The method is able to build a subjectivity lexicon by using a small seed set of subjective words, an online dictionary, and a small raw corpus, coupled with a bootstrapping process that ranks new candidate words based on a similarity measure. Experiments performed with a rule-based sentence level subjectivity classifier show an 18% absolute improvement in F-measure as compared to previously proposed semi-supervised methods.

As many popular text genres such as blogs or news contain opinions by multiple sources and about multiple targets, finding the sources and targets of subjective expressions becomes an important sub-task for automatic opinion analysis systems. We argue that while automatic semantic role labeling systems (ASRL) have an important contribution to make, they cannot solve the problem for all cases. Based on the experience of manually annotating opinions, sources, and targets in various genres, we present linguistic phenomena that require knowledge beyond that of ASRL systems. In particular, we address issues relating to the attribution of opinions to sources; sources and targets that are realized as zero-forms; and inferred opinions. We also discuss in some depth that for arguing attitudes we need to be able to recover propositions and not only argued-about entities. A recurrent theme of the discussion is that close attention to specific discourse contexts is needed to identify sources and targets correctly.