The document summarizes the results of an unsupervised text analysis of proceedings from the 4th All Africa Conference on Animal Agriculture using Leximancer software. Key findings included:
1) Five central themes and 20 main concepts were identified, including "animals", "livestock", and "production".
2) Concepts were grouped into clusters by similarity, and frequency of individual concepts and co-occurrences were determined.
3) Leximancer was able to efficiently analyze large amounts of text data and provide objective, previously unknown insights without human bias. However, the analysis was not exhaustive and did not represent author intentions.
Leximancer Analysis of 4th AACAA Conference Proceedings
1. A CONTENT EVALUATION OF THE PROCEEDINGS OF THE 4th
ALL
AFRICA CONFERENCE ON ANIMAL AGRICULTURE (AACAA):
AN UNSUPERVISED LEXIMANCER™ ANALYSIS
5th
AACAA, Addis Ababa: Ethiopia, 25-28 October 2010
Percy Madzivhandila and Garry Griffith
2. Presentation Outline
• Introduction
• Gaps and an Opportunity
• Aim
• Research Questions
• Data and Method
• Results
• Brief Discussion
• Conclusions
• Study Limitations
3. Introduction
• AACAA allow researchers to further develop and
showcase their work
• After the conference is over, very little is known
about lessons learned at the aggregate level
• Available information management and
knowledge generation technologies are not used
in this context
– i.e., Animal Agriculture & conference proceedings
4. Gaps and an Opportunity
• Generation of knowledge [from the proceedings] can be
daunting and it is often difficult to synthesize and validate
(Hearst 1999, 2003; Smith and Humphreys 2006):
– Magnitude and diversity of contributed papers
– The limitation of a person’s reading speed ability
– Lack the resources (or knowledge about such tools) to analyse
large text data objectively
– Problem with human subjectivity when analyzing text data
• Therefore, unsupervised text data mining methods are
entering knowledge generation lexicon (Lin and Richi
2007; Smith 2000, 2003)
5. Aim
• To identify and analyse recurring issues
raised to address the 4th
AACAA theme
– The theme was: The role of biotechnology in
animal agriculture to address poverty in
Africa: Opportunities and challenges.
6. Research Questions
• What messages emerged (i.e., lessons)
from the 4th
AACAA conference
proceedings?
• What is the potential of Leximancer™ in
establishing credible conference
messages and/or lessons?
7. Data
• The entire proceedings from the 4th
AACAA (Arusha, Tanzania)
– Edited by Rege et al. (2006)
8. Method
• Approach: Unsupervised text data mining technique
– No predetermined categories were imposed on the data through
coding
• Computer software used: Leximancer™ (see www.leximancer.com)
1. Editing of concepts
– It is possible within the software to delete, combine or add new
concepts, However:
• The decision was made to retain only those concepts identified by
the software.
• But the concepts that are similar semantically (i.e., in singular and
plural forms) were merged
2. Data analysis
– Concept maps were augmented by using a three slide bars
embedded in the software to adjust theme size, concept points size
and rotation
9. Results
The entire analysis allowed us to illustrate five
types of information :
• The central theme(s)
• The main concepts (set at 20% concept size)
• The thematic group of concepts that
demonstrate similarity (i.e., clusters)
• Frequency within which these concepts occur
(i.e., ranking)
• Frequency of co-occurring concepts
17. Discussion
• Leximancer™ Version 2.25 (2005) was able to deal with large
amounts of unstructured text data
– It extracted previously unknown, useful and important
concepts and categorized the information that might be
missed by manual methods
• It easily enabled us to provide information about the
results of the content analysis in a number of ways
– E.g., superimposing of thematic circles enriched the viewing of
the initial analytic description
• It enabled us to discover information within the document
objectively without being influenced by human biases
18. Conclusion
• This study results can provide a most
valued learning environment for policy
makers, decision makers and practitioners
– A message at the aggregate level nexus
• We posit that Leximancer™ has an
established role in the content analysis of
large and diverse text data
19. Study Limitations
• Analysis does not purport to be a
complete analysis of the proceedings
– The results reported are by no means
exhaustive
• The study does not claim to represent the
intentions of the authors of the
proceedings' papers
21. References
• Hearst, M. (1999), 'Untangling Text Data Mining'. <
http://people.ischool.berkeley.edu/~hearst/papers/acl99/acl99-tdm.html>, accessed 18 May 2010.
• --- (2003), 'What is Text Mining?'. <http://www.ischool.berkeley.edu/~hearst/textmining.html.>,
accessed 18 May 2010.
• Leximancer (2005), 'Leximancer Version 2.25 Manual'. <<
http://www.leximancer.com/documents/Leximancer2_Manual.pdf.>>, accessed 12 May 2010.
• Lin, C. and Richi, N. (2007), 'A Case Study of Failure Mode Analysis with Text Mining Methods',
in K.-L. Ong, W. Li; and J. Gao (eds.), 2nd International Workshop on Integrating Artificial
Intelligence and Data Mining (AIDM) (Gold Coast, Queensland: CRPIT), 49-60.
• Smith, A. E. (2000), 'Machine Mapping of Document Collections: the Leximancer System', The
5th Australasian Document Computing Symposium (Sunshine Coast, Queensland: Australasian
Document Computing Symposium).
• --- (2003), 'Automatic extraction of semantic networks from text using Leximancer', The North
American Chapter of the Association for Computational Linguistics (HLT-NAACL) (Edmonton,
ACL: HLT-NAACL), 23-24.
• Smith, A.E. and Humphreys, M.R. (2006), 'Evaluation of unsupervised semantic mapping of
natural language with Leximancer concept mapping', Behaviour Research Methods, 38 (2), 262-
79.
• Rege, J.E.O.; Nyamu A.M. and Sendali, G. (eds) (2005), ‘The role of biotechnology in animal
agriculture to address poverty in Africa: Opportunities and challenges’, Proceedings of the 4th
All
Africa Conference on Animal agriculture and the 31st
Annual Meeting of the Tanzania Society
for Animal Production (Arusha:AACAA)