Content analysis

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Content analysis refers to a family of qualitative data analysis methods or to various forms of quantitative analysis.

“Content analysis (sometimes called textual analysis when dealing exclusively with text) is a standard methodology in the social sciences for studying the content of communication. Earl Babbie defines it as "the study of recorded human communications, such as books, websites, paintings and laws." Harold Lasswell formulated the core questions of content analysis: "Who says what, to whom, why, to what extent and with what effect?." Ole Holsti (1969) offers a broad definition of content analysis as "any technique for making inferences by objectively and systematically identifying specified characteristics of messages."” (Wikipedia, retrieved nov 1 2007)

See also:

This entry should be split into two different articles: qualitative content analysis and machine analysis (e.g. text mining) - Daniel K. Schneider 14:01, 12 March 2012 (CET).


(Semi-) manual qualitative data analysis

Quantitative analysis of large corpus

  • TAPoR is a reimagining of the original TAPoR (Text Analysis Portal for Research). It is a both a resource for discovery and a community. The TAPoR team has created a place for Humanities scholars, students and others interested in applying digital tools to their textual research to find the tools they need, contribute their experience and share new tools they have developed or used with others. Best overal resource for textual research.
  • Text mining (German Wikipedia). Better, if you speak German.
  • Text Insight. serves as a research and academic portal for those doing qualitative analysis and text analytics. Main focus of the site is the Leximancer tool However, all researchers, students, academics, and commercial entities are welcome to use this portal and its resources.


See other wiki pages of interest

These pages include specialised technologies

  • latent semantic analysis and indexing, a family of analysis techniques that that assume that a text contains a semantic structure through a kind vector space model and some kind of factor analysis that identifies relationships between terms.

List of tools

See: Portal: Data mining and learning analytics tools'

Undocumented tools in the portal above

  • Leximancer, allows to summarize and navigate large text data (e.g. a wiki site) with various visualization tools. (commercial, $750 AUD single license or $150 one-month online)
  • TagHelper is one of more popular tools for analysing texts in education. It also has been integrated into learning systems (Dönmez, 2005; Kumar, 2007). LightSide can be considered a successor.

Sites with more links to tools

Simple word graphics


(to do )

  • Rose, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F. (In Press). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning , International Journal of Computer Supported Collaborative Learning.
    • Taghelper tools
  • Dönmez, P., Rosé, C., Stegmann, K., Weinberger, A., & Fischer, F. (2005). Supporting CSCL with automatic corpus analysis technology. Paper presented at the Proceedings of th 2005 Conference on Computer Support for Collaborative Learning: Learning 2005: The Next 10 Years! (pp. 125 – 134), Taipei, Taiwan.
    • Use of TagHelper
  • Kumar, R., Rosé, C., Wang, Y.-C., Joshi, M., & Robinson, A. (2007). Tutorial dialogue as adaptive collaborative learning support. Paper presented at the Proceeding of the 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (pp. 383 – 390).
    • Use of TagHelper

Text and data mining

  • Xiaojun Chen, Yunming Ye, Graham Williams, and Xiaofei Xu, A Survey of Open Source Data Mining Systems. PDF

Analysis of text quality


Analysis of on-line interactions

  • De Wever, B., Schellens, T., Valcke, M., and Van Keer, H. 2006. Content analysis schemes to analyze transcripts of online asynchronous discussion groups: a review. Comput. Educ. 46, 1 (Jan. 2006), 6-28. DOI=
  • Pena-Shaff, J. B. and Nicholls, C. 2004. Analyzing student interactions and meaning construction in computer bulletin board discussions. Computers and Education 42, 3 (Apr. 2004), 243-265. DOI=
  • Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (2001). Methodological Issues in the Content Analysis of Computer Conference Transcripts. International Journal of Artificial Intelligence in Education, 12(1), 8-22. PDF