Mining Fine-grained Opinion Expressions with Shallow Parsing

Opinion analysis deals with public opinions and trends, but subjective language is highly ambiguous. In this paper, we follow a simple data-driven technique to learn fine-grained opinions. We select an intersection set of Wall Street Journal documents that is included both in the Penn Discourse Tree Bank (PDTB) and in the Multi-Perspective Question Answering (MPQA) corpus. This is done in order to explore the usefulness of discourselevel structure to facilitate the extraction of fine-grained opinion expressions. Here we perform shallow parsing of MPQA expressions with connective based discourse structure, and then also with Named Entities (NE) and some syntax features using conditional random fields; the latter feature set is basically a collection of NEs and a bundle of features that is proved to be useful in a shallow discourse parsing task. We found that both of the feature-sets are useful to improve our baseline at different levels of this fine-grained opinion expression mining task.