Text Embedding for Political Science Research

• In Event: Poster Session: Posters in Political Methodology

Abstract

Text embedding, a rapidly advancing technique for converting text into meaningful vectors, has witnessed significant development in the last two years. Despite this progress, social scientists have yet to fully grasp its potential in reshaping text analysis practices. This research sheds light on how text embedding can enhance fully automated content analysis for social scientists. Firstly, text embedding enables scholars to more effectively assess relationships among text contents and the associations between text contents and text covariates. Notably, unlike conventional approaches like structural topic models, identifying these relationships does not hinge on algorithmic clustering that coincidentally aligns with scholars' research interests. Secondly, text embedding facilitates text clustering, offering a faster, more accurate, and user-friendly alternative to popular probabilistic topic models. It liberates scholars from extensive text pre-processing and significantly accelerate the process of selecting key hyperparameters. Additionally, utilizing word embedding for clustering allows scholars to efficiently cluster short texts, providing an improved approach for analyzing social media data. This research underscores the transformative potential of text embedding in advancing the methodologies employed by social scientists in automated content analysis.

Author

• Jianjun Yu