Catpac

Catpac is a computer program that analyzes text samples to identify key concepts contained within the sample. It was conceived chiefly by Richard Holmes, a Michigan State computer programmer and Dr. Joseph Woelfel, a University at Albany and University at Buffalo sociologist for the analysis of attitude formation and change in the sociological context. Contributions by Rob Zimmelman, an undergraduate and graduate student at the University of Albany, from 1981 to 1984 on the Univac 1100 mainframe, included the inclusion of the CATPAC software in the Galileo*Telegal system, text-labeling and porting of CATPAC output for the Galileo system of paired-comparison conceptual visualization. CATPAC and the Galileo system are still in commercial use today, and with recent data capture and visualization contributions, continues to grow. Contributions by other students at the university resulted in the software that is still in commercial use today. It uses text files as input and produces output such as word and alphabetical frequencies as well as various types of cluster analysis.[1]

Design

Catpac is a self-organizing, i.e. unsupervised, interactive activation and competition (IAC) artificial neural network used for text analysis.[2][3] The program generates a multidimensional scalar output organizing words throughout the text by creating a weighted word-by-word matrix that establishes the eigenvector centralities of concepts.[4] The word-by-word matrix represents the relationship between one word and the occurrence of another.[5] Catpac identifies important words and patterns based on the organization of the text.[2] This process mimics the connections between neurons in a human brain, strengthening connections through conditioning to generate a pattern of similarities among all words within a body of text.[2]

Use

Catpac has been used in commercial studies, in academic scholarship to investigate massive textual data sets,[6][7] as a strong semantic network analysis tool,[4][5][8] for longitudinal analyses,[4][8][9][10][11] for multilingual analyses,[12][13] as a predictor of media usage[14] and as a powerful content analysis tool.[15][16]

Availability

Catpac, conceived as an improvement to simple word-count software more than 30 years ago, is currently available in windows 32 bit format.[2]

gollark: ++experimental_qa football Where is football played?
gollark: ++experimental_qa "normal distribution" What other bell curve shaped distributions exist?
gollark: ++experimental_qa "normal distribution" variance
gollark: ++experimental_qa bee Why is bee?
gollark: ++experimental_qa beeoid what is bee?

References
  1. "Archived copy". Archived from the original on 2012-07-01. Retrieved 2010-11-26.CS1 maint: archived copy as title (link)
  2. Woelfel, Joseph. "Catpac II User's Guide" (PDF) (Version 2.0 ed.). The Galileo Company.
  3. http://www.galileoco.com/literature/Wolfpak10a.pdf
  4. Egnoto, M.; Nam, Y.; Vishwanath, A (November 2010). A longitudinal analysis of the newspaper coverage of cell phones. National Communication Association Conference. San Francisco, CA.
  5. Doerfel, M. L.; Barnett, G. A. (1999). "A semantic network analysis of the International Communication Association". Human Communication Research. 25 (4): 589–603. CiteSeerX 10.1.1.531.2227. doi:10.1111/j.1468-2958.1999.tb00463.x.
  6. Chen, H.; Evans, C.; Battleson, B.; Zubrow, E.; Woelfel, J. (10 October 2011). "Procedures for the precise analysis of massive textual datasets". Communication & Science Journal.
  7. Doerfel, M. L.; Barnett, G. A. (1996). "The use of CATPAC for textual analysis". Field Methods. 8 (2): 4–7. doi:10.1177/1525822x960080020501.
  8. Ortega, C.R.; Egnoto, M.J. (2011). Longitudinal analysis of press coverage of violent video games: Assessing agenda-setting via semantic and LIWC analyses. NYSCA conference.
  9. Kim, J.H.; Su, T-Y.; Hong, J. (2007). "The influence of geopolitics and foreign policy on the U.S. and Canadian media: An analysis of newspaper coverage of Sudan's Darfur conflict". The Harvard International Journal of Press/Politics. 12 (3): 87–95. doi:10.1177/1081180x07302972.
  10. Murphy, P.; Maynard, M. (2000). "Framing the genetic testing issue: Discourse and cultural clashes among policy communities". Science Communication. 22 (2): 133–153. doi:10.1177/1075547000022002002.
  11. Rosen, D.; Woelfel, J.; Krikorian, D.; Barnett, G.A. (2003). "Procedures for analyses of online communities". Journal of Computer-Mediated Communication. 8 (4).
  12. Evans, C.; Chen, H.; Battleson, B.; Wölfel, J.K.; Woelfel, J. (2008). Neural networks for pattern recognition in multilingual text. International Network for Social Network Analysis (INSNA) Sunbelt conference. St. Pete Beach, FL.
  13. Evans, C.; Chen, H.; Battleson, B.; Wölfel, J.K.; Woelfel, J. (2010). Unsupervised artificial neural networks for pattern recognition in multilingual text. Amherst, NY: RAH Press.
  14. Cheong, P.; Hwang, J.; Elbirt, B.; Chen, H.; Evans, C.; Woelfel, J (2010). "Media use as a function of identity: The role of the self concept in media usage". In Hinner, M. (ed.). Freiberger beiträge zur interkulturellen und wirtschaftskommunikation [A forum for general and intercultural business communication]. The interrelationship of business and communication. 6. Berlin: Peter Lang. pp. 365–381.
  15. Krippendorff, K. (2004). Content analysis: An introduction to its methodology (2nd ed.). Thousand Oaks, CA: SAGE Publications.
  16. Neuendorf, K. "Quantitative text analysis programs". The Content Analysis Guidebook Online. Archived from the original on 1 July 2012. Retrieved 26 November 2010.
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