Social scientists have long conducted content analysis by using their substantive knowledge and manually coding documents. In recent years, however, fully automated content analysis based on probabilistic topic models has become increasingly popular because of their scalability. Unfortunately, applied researchers find that these models often fail to yield topics of their substantive interest by inadvertently creating multiple topics with similar content and combining different themes into a single topic. In this paper, we empirically demonstrate that providing topic models with a small number of keywords can substantially improve their performance. The proposed keyword assisted topic model (keyATM) offers an important advantage that the specification of keywords requires researchers to label topics prior to fitting a model to the data. This contrasts with a widespread practice of post-hoc topic interpretation and adjustments that compromises the objectivity of empirical findings. In our applications, we find that the keyATM provides more interpretable results, has better document classification performance, and is less sensitive to the number of topics than the standard topic models. Finally, we show that the keyATM can also incorporate covariates and model time trends. An open-source software package is available for implementing the proposed methodology.
- Shusei Eshima, Kosuke Imai, and Tomoya Sasaki. 2020. ``Keyword Assisted Topic Models.’’ Working Paper, arXiv:2004.05964.
- American Political Science Association, Annual Meeting
- San Francisco, CA, September 2020
- Presentation (Slides)
- Annual Conference of the Society for Political Methodology
- Virtual conference, July 2020
- International Methods Colloquium
- Midwest Political Science Association, Annual Meeting
- Chicago, IL, April 2020
- Presentation (cancelled due to the COVID-19)
- Applied Statistics Workshop
- Institute for Quantitative Social Science, February 2020
- Japanese Society for Quantitative Political Science, Winter Meeting
- Waseda University, Tokyo, January 2020