Whether you call it ‘content analysis’, ‘textual data labeling’, ‘hand-coding’, or ‘tagging’, a lot more researchers and data science teams are starting up annotation projects these days. Learn how to avoid potential pitfalls.

The "Swahili Lexicon for Sentiment Analysis" project, funded by a Sage Concept Grant, aims to develop and test a Swahili Lexicon annotated by native Swahili speakers for text mining, particularly sentiment analysis.  

The 2020 SAGE Ocean Concept Grant program drew over 140 applications from all over the world. In this blog post, we’re giving you an insight into our judging criteria and sharing the most common reasons why applications did not progress further, to serve as feedback for this year’s applicants and guidance for future applicants.

Our product development team at SAGE Ocean is excited to present a new opportunity: we are seeking a SAGE Ocean Fellow, who will work with us to refine and test a new product for academics that apply automated text analysis techniques in their research.

Text is everywhere, and everything is text. More textual data than ever before are available to computational social scientists—be it in the form of digitized books, communication traces on social media platforms, or digital scientific articles. Researchers in academia and industry increasingly use text data to understand human behavior and to measure patterns in language. Techniques from natural language processing have created a fertile soil to perform these tasks and to make inferences based on text data on a large scale.

‘From text to data - new ways of reading’ was a 2-day event organised by the National Library of Sweden, the National Archives and Swe-Clarin. The conference brought together librarians, digital collection curators, and scholars in digital humanities and computational social science to talk about the tools and challenges involved in large scale text collection and analysis.