Books and compendiums can be bought in Akademika bookstore at Blindern campus. You will need a valid semester card to buy compendiums.
Compendium
Blei, D. M. & Lafferty, J. (2009). Topic models. In: Text mining: classification, clustering, and applications, 10, eds Ashok N. Srivastava, Mehran Sahami, 71–95.
Krippendorff, K. (2012). Content analysis: An introduction to its methodology. Sage. Part 1.
Online articles
Benoit, K., Conway, D., Lauderdale, B. E., Laver, M., & Mikhaylov, S. (2016). Crowd-sourced text analysis: reproducible and agile production of political data. American Political Science Review, 110 (02), 278-295
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
Feinerer, I., Hornik, K., & Meyer, D. (2008). Text mining infrastructure in R. Journal of Statistical Software, 25(5), 1–54.
Feinerer, I. (2017). Introduction to the tm package text mining in R
Feng, G. C. (2014). Intercoder Reliability Indices: Disuse, Misuse, and Abuse. Quality & Quantity, 48(3), 1803–1815.
Gerrish, S. & Blei, D. M. (2011). Predicting legislative roll calls from text. In Proceedings of the 28th international conference on machine learning (icml-11) (pp. 489–496).
Grimmer, J. & Stewart, B. M. (2013). Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297.
Grimmer, J. & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297.
Grimmer, J. (2013). Appropriators not position takers: the distorting effects of electoral incentives on congressional representation. American Journal of Political Science, 57(3), 624–642.
Grimmer, J., & King, G. (2011). General purpose computer-assisted clustering and conceptualization. Proceedings of the National Academy of Sciences, 108 (7), 2643-2650.
Grimmer, J. (2010). A Bayesian hierarchical topic model for political texts: measuring expressed agendas in senate press releases. Political Analysis, 18(1), 1–35.
Hopkins, D. J. & King, G. (2010). A method of automated nonparametric content analysis for social science. American Journal of Political Science, 54 (1), 229–247.
Jackman, S. (2006). Data from the web into r. The Political Methodologist, 14(2), 11–15.
Lang, D. T. (2007). R as a web client–the rcurl package. Journal of Statistical Software
Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97 (02), 311–331.
Lowe, W. (2008). Understanding Wordscores. Political Analysis, 16(4), 356–371.
Lucas, C., Nielsen, R. A., Roberts, M. E., Stewart, B. M., Storer, A., & Tingley, D. (2015). Computer-assisted text analysis for comparative politics. Political Analysis, 23(2), 254-277.
Mikhaylov, S., Laver, M., & Benoit, K. R. (2012). Coder reliability and misclassification in the human coding of party manifestos. Political Analysis, 20(1), 78–91.
Roberts, C. W. (2000). A conceptual framework for quantitative text analysis. Quality and Quantity, 34(3), 259–274.
Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder‐Luis, J., Gadarian, S. K., ... & Rand, D. G. (2014). Structural Topic Models for Open‐Ended Survey Responses. American Journal of Political Science, 58 (4), 1064-1082.
Slapin, J. B. & Proksch, S.-O. (2008). A scaling model for estimating time-series party positions from texts. American Journal of Political Science, 52(3), 705–722.
Silge, J., & Robinson, D. (2017) Text Mining with R: A Tidy Approach
Online articles:
- are available through the University Library databases.
- are limited to computers that are connected UiO's network. You can get access from home and when travelling.
- are easy to find. Tutorials and help to access journals and other restricted library Resources.