Connecting Data Science and Qualitative Interview Insights through Sentiment Analysis to Assess Migrants' Emotion States Post-Settlement (1609.08776v1)

Abstract: Large-scale survey research by social scientists offers general understandings of migrants' challenges and provides assessments of post-migration benchmarks like employment, obtention of educational credentials, and home ownership. Minimal research, however, probes the realm of emotions or "feeling states" in migration and settlement processes, and it is often approached through closed-ended survey questions that superficially assess feeling states. The evaluation of emotions in migration and settlement has been largely left to qualitative researchers using in-depth, interpretive methods like semi-structured interviewing. This approach also has major limitations, namely small sample sizes that capture limited geographic contexts, heavy time burdens analyzing data, and limits to analytic consistency given the nuances of qualitative data coding. Information about migrant emotion states, however, would be valuable to governments and NGOs to enable policy and program development tailored to migrant challenges and frustrations, and would thereby stimulate economic development through thriving migrant populations. In this paper, we present an interdisciplinary pilot project that offers a way through the methodological impasse by subjecting exhaustive qualitative interviews of migrants to sentiment analysis using the Python NLTK toolkit. We propose that data scientists can efficiently and accurately produce large-scale assessments of migrant feeling states through collaboration with social scientists.