Editing Content analysis (section)

== Computational tools ==

With the rise of common computing facilities like PCs, computer-based methods of analysis are growing in popularity.<ref>Pfeiffer, Silvia, Stefan Fischer, and Wolfgang Effelsberg. "[https://ub-madoc.bib.uni-mannheim.de/794/1/TR-96-008.pdf Automatic audio content analysis]." Technical Reports 96 (1996).</ref><ref>Grimmer, Justin, and Brandon M. Stewart. "[https://web.stanford.edu/~jgrimmer/Text14/tc5.pdf Text as data: The promise and pitfalls of automatic content analysis methods for political texts]." Political analysis 21.3 (2013): 267-297.</ref><ref>Nasukawa, Tetsuya, and Jeonghee Yi. "[http://nactem.ac.uk/files/workshop06/TAKMIandSA2006.pdf Sentiment analysis: Capturing favorability using natural language processing]." Proceedings of the 2nd international conference on Knowledge capture. ACM, 2003.</ref> Answers to open ended questions, newspaper articles, political party manifestos, medical records or systematic observations in experiments can all be subject to systematic analysis of textual data.

By having contents of communication available in form of machine readable texts, the input is analyzed for frequencies and coded into categories for building up inferences.

Computer-assisted analysis can help with large, electronic data sets by cutting out time and eliminating the need for multiple human coders to establish inter-coder reliability. However, human coders can still be employed for content analysis, as they are often more able to pick out nuanced and latent meanings in text. A study  found that human coders were able to evaluate a broader range and make inferences based on latent meanings.<ref>{{Cite journal|last=Conway|first=Mike|date=March 2006|title=The Subjective Precision of Computers: A Methodological Comparison with Human Coding in Content Analysis|journal=Journalism & Mass Communication Quarterly|volume=83|issue=1|pages=186–200|doi=10.1177/107769900608300112|s2cid=143292050|issn=1077-6990}}</ref>