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.

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

10. Results 1: Underpinning Themes

11. Results 2: Main Concepts

12. Results 3: Thematic & Concepts Map

13. Results 4: Ranked Concepts
#
Concept
Absolute
Count
Relative
Count
1 animals 771 100%
2 livestock 573 74.3%
3 production 516 66.9%
4 research 452 58.6%
5 countries 364 47.2%
6 biotechnology 350 45.3%
7 development 346 44.8%
8 milk 339 43.9%
9 genetic 330 42.8%
10 level 315 40.8%
11 systems 305 39.5%
12 breeds 295 38.2%
13 Africa 280 36.3%
14 cattle 274 35.5%
15 products 269 34.8%
16 farmers 264 34.2%
17 Proceedings 255 33%
18 disease 238 30.8%
19 health 229 29.7%
20 goat 228 29.5%
21 information 215 27.8%
22 food 214 27.7%
23 study 205 26.5%
24 African 198 25.6%
25 resources 191 24.7%

14. Results 5: Co-occurrence of Concepts
Animal(s)
Concept Absolute Count Relative Count
1 livestock 218 28.2%
2 production 178 23%
3 genetic 149 19.3%
4 health 147 19%
5 research 130 16.8%
6 biotechnology 128 16.6%
7 disease 109 14.1%
8 countries 101 13%
9 development 96 12.4%
10 products 93 12%
11 resources 91 11.8%
12 breeds 85 11%
13 systems 84 10.8%
14 food 76 9.8%
15 farmers 75 9.7%
16 cattle 75 9.7%
17 Africa 74 9.5%
18 milk 70 9%
19 potential 69 8.9%
20 indigenous 66 8.5%
21 level 66 8.5%
22 breeding 63 8.1%
23 agriculture 57 7.3%
24 human 56 7.2%
25 National 54 7.0%

15. Results 5: Co-occurrence of Concepts
Milk
Concept Absolute Count Relative Count
1 production 103 30.3%
2 goat 86 25.3%
3 animals 70 20.6%
4 dairy 59 17.4%
5 meat 56 16.5%
6 protein 52 15.3%
7 level 51 15%
8 products 47 13.8%
9 cows 43 12.6%
10 cattle 39 11.5%
11 study 37 10.9%
12 livestock 36 10.6%
13 systems 35 10.3%
14 quality 34 10%
15 farmers 32 9.4%
16 high 30 8.8%
17 breeds 29 8.5%
18 Africa 29 8.5%
19 control 25 7.3%
20 genetic 23 6.7%
21 human 22 6.4%
22 countries 21 6.1%
23 health 20 5.8%
24 small 20 5.8%
25 food 20 5.8%

16. Concept
Absolute
Count
Relative
Count
1 animals 128 36.5%
2 research 122 34.8%
3 livestock 110 31.4%
4 countries 82 23.4%
5 development 77 22%
6 production 68 19.4%
7 products 67 19.1%
8 application 57 16.2%
9 health 56 16%
10 Africa 51 14.5%
11 food 47 13.4%
12 national 45 12.8%
13 agricultural 42 12%
14 genetic 42 12%
15 poor 41 11.7%
16 agriculture 41 11.7%
17 potential 40 11.4%
18 developing 40 11.4%
19 resources 39 11.1%
20 poverty 38 10.8%
20 science 36 10.2%
21 human 35 10%
22 issues 32 9.1%
23 international 31 8.8%
24 African 29 8.2%
25 risk 29 8.2%
Results 5: Co-occurrence of Concepts
Biotechnology

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

20. Thank You

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)