Text analysis techniques, including sentiment analysis, topic modeling, and named entity recognition, have been increasingly used to probe patterns in a variety of text-based documents, such as books, social media posts, and others. This blog post introduces Twitter text analysis, but is not intended to cover all of the aforementioned topics. The tutorial is broken down into two parts. In this very first post, I...
How can we use neural networks to create meaningful representations of words? The bag-of-words is limited in its ability to characterize text, because it does not utilize word context.
This two-part workshop series will prepare participants to move forward with research that uses text analysis, with a special focus on humanities and social science applications.
I’m a D-Lab GSR and a graduate student in The Goldman School’s Master of Public Policy/The I School’s Graduate Certificate in Applied Data Science. I have 5 years of experience working on data problems in government and nonprofits. I’m interested in social policy, program evaluation, and computational methods. Python is my principal language, but I’ve developed experience using and teaching a variety of other tools, including R, Excel, Tableau, and JavaScript. I deeply enjoy teaching data science methods and am excited to be a part of the D-Lab.
Former D-Lab Postdoc and Senior Data Science Fellow
Berkeley Law
Aniket Kesari was a postdoc and data science fellow at D-Lab. He is currently a research fellow at NYU’s Information Law Institute, and will join the faculty of Fordham Law School in 2023. His research focuses on law and data science, with particular interests in privacy, cybersecurity, and consumer protection.
How can we use neural networks to create meaningful representations of words? The bag-of-words is limited in its ability to characterize text, because it does not utilize word context.
