The five pitfalls of document labeling - and how to avoid them

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. Many want human judgment labeled onto text to train AI (via supervised machine learning approaches). Others have tried automated text analysis and found it wanting. Now they’re looking for ways to label text that aren’t so hard to interpret and explain.

No more tradeoffs: The era of big data content analysis has come

For centuries, being a scientist has meant learning to live with limited data. People only share so much on a survey form. Experiments don’t account for all the conditions of real world situations. Field research and interviews can only be generalized so far. Network analyses don’t tell us everything we want to know about the ties among people. And text/content/document analysis methods allow us to dive deep into a small set of documents, or they give us a shallow understanding of a larger archive. Never both. So far, the truly great scientists have had to apply many of these approaches to help us better see the world through their kaleidoscope of imperfect lenses.