English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Large-scale survey research by social scientists offers generalunderstandings of migrants' challenges and provides assessments ofpost-migration benchmarks like employment, obtention of educationalcredentials, and home ownership. Minimal research, however, probes the realm ofemotions or "feeling states" in migration and settlement processes, and it isoften approached through closed-ended survey questions that superficiallyassess feeling states. The evaluation of emotions in migration and settlementhas been largely left to qualitative researchers using in-depth, interpretivemethods like semi-structured interviewing. This approach also has majorlimitations, namely small sample sizes that capture limited geographiccontexts, heavy time burdens analyzing data, and limits to analytic consistencygiven the nuances of qualitative data coding. Information about migrant emotionstates, however, would be valuable to governments and NGOs to enable policy andprogram development tailored to migrant challenges and frustrations, and wouldthereby stimulate economic development through thriving migrant populations. Inthis paper, we present an interdisciplinary pilot project that offers a waythrough the methodological impasse by subjecting exhaustive qualitativeinterviews of migrants to sentiment analysis using the Python NLTK toolkit. Wepropose that data scientists can efficiently and accurately produce large-scaleassessments of migrant feeling states through collaboration with socialscientists.

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