Sentiment Analysis and Opinion Mining in Collections of Qualitative Data

In social sciences, a tremendous body of data is being collected by observing or interviewing people. Such qualitative data forms a valuable source for later secondary research. One major challenge, though, is the preservation of privacy of the interviewees even after longer time periods of archival storage. Modern sentiment analysis techniques could help to judge the sensitivity of particular textual content and help the data provider to remove sensitive data from unauthorized eyes, thus reducing manual processing of large collections of primary material. Besides, mining opinions enables enhanced data access, e.g., by finding negative attitudes about a topic. In this paper we will describe properties of qualitative social science data with respect to sentiment analysis. We compare it to datasets used in the literature, identify main challenges, and provide directions for solving them. By discussing how to exploit state-of-the-art techniques to leverage the (secondary) exploration of archived qualitative data we hope to foster interdisciplinary dialogue.