1. CATAR-Content Analysis Toolkit for Academic Research
Introduction
Installation
Usage
Interpretation
Case Study
Yuen-Hsien Tseng
National Taiwan Normal University
2018/04/25

2. Content Analysis: Introduction
Related Subjects:
Bibliometrics, Scientometrics, Infometrics
Content analysis in social science
Related Journals
JASIST, Scientometrics, Journal of Infometrics
Related Conferences
ISSI: International Society for Scientometrics and Infometrics
STI: Science and Technology Indicators 2

3. Content Analysis: Motivation
Prior art search and analysis is expected to be done in a half day
From a vice president of a second largest analog IC design company
To know the past, avoid reinventing the wheels, and improve innovation
For strategic planning in S&T
To know highly impact authors, institutions
For speech, cooperation, advise
For evaluation, budget distribution 3

4. Automatic Content Analysis
Long term goal:
Automatic literature analysis, organization, and presentation
To form hypothesis for exploration, verification, and decision making
Related studies:
Structured Abstract in library science (1987)
Automated structured abstract in biology (2007)
Automatic patent analysis (2004, NTCIR)
Sentimental analysis in literature (2010, STI) 4

5. Automatic Content Analysis -Tools(1/2)
CiteSpace
Chao-Mei Chen, Drexel University (2003)
To know the paradigm shift in research
VOSviewer
Nees Jan van Eck and Ludo Waltman (2007)
CWTS of Leiden University 5

6. Automatic Content Analysis -Tools(1/2)
Science Mapping Software Tools: Review, Analysis, and Cooperative Study Among Tools
Cobo, et al, JASIST 2011 paper
Compare nine tools (free, commercial)
Bibexcel, VantagePoint, Sci2 Tool, …
None of them cover all the functions of the others
Scientometrics analysis has a standard procedure (Börner et al 2003)
CATAR released in 2010 (developed since 2004) 6

7. CATAR-Introduction Content Analysis Toolkit for Academic Research
Yuen-Hsien Tseng,
CATAR technical details：
Yuen-Hsien Tseng, Chi-Jen Lin, and Yu-I Lin, "Text Mining Techniques for Patent Analysis", Information Processing and Management, Vol. 43, No. 5, 2007, pp (indexed in ESI as top 1% cited paper in 2017)
Journal clustering of Library and Information Science for subfield delineation using the bibliometric analysis toolkit: CATAR", Scientometrics, Vol. 95, No. 2, pp , May 2013. 7

8. CATAR Analysis Functions
Overview analysis
Topic Clustering based on:
bibliographic coupling
co-word analysis 8

9. CATAR Installation Please check below link for latest update:
C:\CATAR folder:
C:\CATAR\src\ : programs for analysis
C:\CATAR\Source\ : data to be analyzed
C:\CATAR\Result\ : result after analysis
C:\CATAR\doc\ : intermediate result during analysis (just for debugging, no need for end user) 9

10. Preparing Data for Analysis
Delineate the data (the most important step, the second valuable part)
Data from keyword search
Publication records in core journals
Combined search results
Journals + keywords + time limit
Data record verified by domain experts
Data source ready for analysis by CATAR
Records from Web of Science
Patents from USPTO 10

11. ISI WoS Publication Record
Only the fields in red color are used.
Cited References are used in the bibliographic coupling for topic clustering and citation tracking
FN ISI Export Format
VR 1.0
PT J
AU Tseng, SC
Tsai, CC
AF Tseng, Sheng-Chau
Tsai, Chin-Chung
TI On-line peer assessment and the role of the peer feedback: A study of high school computer course
SO COMPUTERS & EDUCATION
LA English
DT Article
DE interactive learning environments; secondary education; learning
communities; improving classroom teaching; peer assessment
ID WORLD-WIDE-WEB; ASSESSMENT SYSTEM; HIGHER-EDUCATION;
STUDENTS; THINKING; SCIENCE; SELF
AB The purposes of this study were to explore the effects and the validity of on-line peer assessment in high schools and …
C1 Natl Chiao Tung Univ, Inst Educ, Hsinchu 300, Taiwan.
Natl Chiao Tung Univ, Ctr Teacher Educ, Hsinchu 300, Taiwan.
RP Tsai, CC, Natl Chiao Tung Univ, Inst Educ, 1001 Ta Hsueh Rd, Hsinchu
300, Taiwan. EM
CR ROTH WM, 1997, SCI EDUC, V6, P373
DOCHY F, 1999, STUD HIGH EDUC, V24, P331 …
NR 23
TC 2
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN
J9 COMPUT EDUC
JI Comput. Educ.
PD DEC
PY 2007
VL 49
IS 4
BP 1161
EP 1174
DI /j.compedu
PG 14
SC Computer Science, Interdisciplinary Applications; Education &
Educational Research
GA 218OF
UT ISI: ER 11

12. Import Fields of WoS AU: authors' names, e.g., Kainz, H; Hofstetter, H
TI: publication title, e.g., Adaption of the main waste water treatment …
SO: journal title, e.g., WATER SCIENCE AND TECHNOLOGY。
DE: keywords given by the authors: large wastewater treatment plant;
ID: identifiers given by WoK to describe the topics of the article
AB: publication's abstract
C1: authors‘ countries, e.g., USA, UK, …
CR: cited references: BALDI F, 1988, WATER AIR SOIL POLL, V38, P111
NR: Number of references, e.g., 3
TC: Times Cited, e.g., 1
PY: publication year, e.g., 1996
SC: source categories e.g.,Environmental Sciences; Water Resources
UT：indexing key given by WoS, e.g., ISI:A1996VF 12

13. Overview Analysis
Parse data and save into DBMS for management, cross tabulation, and verification
Trend Analysis
Trend indicator in terms of the slope of the linear regression based on the publication volume per year
Yuen-Hsien Tseng, Yu-I Lin, Yi-Yang Lee, Wen-Chi Hung, and Chun-Hsiang Lee, " A Comparison of Methods for Detecting Hot Topics", Scientometrics, Vol. 81, No. 1, Oct. 2009, pp
Command to be executed:
C:\CATAR\src>perl –s automc.pl -OOA SE ..\Source_Data\SE\data
Command option
Folder name for result
Path to the data for analysis 13

14. DOS Command Find and run cmd.exe in MS Windows Change drive to C: C:
Change folder to CATAR: cd \CATAR
Change working directory: cd src
Absolute path: C:\CATAR\Source_Data\SE\data
Relative path: if working directory is under \CATAR\src then the path to data is ..\Source_Data\SE\data 14

15. Overview Analysis Example
Results in : C:\CATAR\Result\SE\_SE_by_field.xls
papers
Search Command #1 54
SO=(Journal of the Learning Sciences) #2
640
SO=(Computers & Education) #3
238
SO=(Science Education) #4
187
SO=(Journal of Computer Assisted Learning) #5
249
SO=(Journal of Research in Science Teaching) #6
365
SO=(British Journal of Educational Technology) #7
326
SO=(Educational Technology & Society) #8
144
SO=(ETR&D-Educational Technology Research And Development) #9
422
SO=(International Journal of Science Education)
#10
SO=(Research in Science Education)
#11
143
SO=(Innovations in Education and Teaching International)
#12
2,912
#1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9 or #10 or #11
Document Type=(Article) Databases=SCI-EXPANDED, SSCI, A&HCI Timespan= 15

16. Year Production: Top 8 Countries
USA UK
TAIWAN
AUSTRALIA
CANADA
TURKEY
NETHER LANDS
SPAIN
2004 12 3 1 6 4
2005
138 69 36 38 16 14 29 15
2006
139 63 31 25 18 19 13
2007
173 70 61 43 28 20 21
2008
204 72
108 44 34
2009
198 71 84 42 24
2010 7 2
total
870
352
328
141
116
114 98 16

17. Most Productive Authors: Top 10NC TC IF FC
FTC
FIF
Tsai, CC 37
227
6.14
17.6
104.9
5.96
Roth, WM 18 61
3.39
7.7
25.7
3.34
Koper, R 15 60
4.00
3.8
21.4
5.63
Hwang, GJ 14 94
6.71
3.7
27.3
7.38
Valcke, M 13
165
12.69
4.3
53.4
12.42
Lee, O 12 93
7.75
3.2
23.0
7.19
Chang, CY 11 49
4.45
5.2
25.6
4.92
Huang, YM 42
3.82
3.6
12.8
3.56
Sadler, TD
110
10.00
4.7
48.6
10.34
Chang, KE 56
5.09
3.3
16.6
5.03
AU Tseng, SC
Tsai, CC
Tseng, SC : 0.5
Tsai, CC : 0.5
AU Tseng, SC
Tsai, CC
Tseng, SC : 1
Tsai, CC : 1
NC=Normal Count: each co-author is counted as a single author
FC=Fractional Count: all the co-authors are counted as a single author
IF =TC/NC, FIF=FTC/FC 17

18. Most Productive Institutes: Top 15NC TC IF FC
FTC
FIF
Natl Taiwan Normal Univ 61
220
3.61
45.6
157.4
3.45
Nanyang Technol Univ 52
217
4.17 37
149.2
4.03
Open Univ 50
265
5.30
41.3
234.4
5.68
Natl Cent Univ 46
276
6.00
29.2
164.1
5.62
Indiana Univ 39
315
8.08
22.8
171.0
7.50
Natl Taiwan Univ Sci & Technol 35
212
6.06 22
117.8
5.35
Natl Cheng Kung Univ 34
108
3.18
27.4
90.3
3.30
Middle E Tech Univ 33 87
2.64
24.3
70.3
2.89
Florida State Univ 32
145
4.53
21.2
75.0
3.54
Curtin Univ Technol 31 85
2.74
18.9
51.2
2.71
Univ Georgia
138
4.45
19.3
81.7
4.23
Natl Chiao Tung Univ 29
150
5.17
18.6
93.8
5.04
Univ London
168
5.79
20.9
83.6
4.00
Arizona State Univ 28
104
3.71
18.4
62.8
3.41
Weizmann Inst Sci 27
153
5.67
20.7
121.3
5.86
Data are from the C1 field of each record:
C1 Natl Chiao Tung Univ, Inst Educ, Hsinchu 300, Taiwan 18

19. Data are from the CR field of each record:
Most Cited References
*NAT RES COUNC, 1996, NAT SCI ED STAND
245
LEDERMAN NG, 1992, J RES SCI TEACH, V29, P331 63
LAVE J, 1991, SITUATED LEARNING LE
157
*NRC, 1996, NAT SCI ED STAND
VYGOTSKY LS, 1978, MIND SOC DEV HIGHER
131
DRIVER R, 2000, SCI EDUC, V84, P287 61
BROWN JS, 1989, EDUC RES, V18, P32
113
DRIVER R, 1996, YOUNG PEOPLES IMAGES 59
WENGER E, 1998, COMMUNITIES PRACTICE
109
MILLAR R, 1998, 2000 SCI ED FUTURE
*AM ASS ADV SCI, 1993, BENCHM SCI LIT 93
LEMKE JL, 1990, TALKING SCI LANGUAGE
POSNER GJ, 1982, SCI EDUC, V66, P211 78
*NAT RES COUNC, 2000, INQ NAT SCI ED STAND 57
SHULMAN LS, 1986, EDUC RES, V15, P4 76
LINCOLN YS, 1985, NATURALISTIC INQUIRY 52
COHEN J, 1988, STAT POWER ANAL BEHA 70
BROWN AL, 1992, J LEARN SCI, V2, P141
SHULMAN LS, 1987, HARVARD EDUC REV, V57, P1 67
COLLINS A, 1989, KNOWING LEARNING INS, P453
Data are from the CR field of each record:
CR ROTH WM, 1997, SCI EDUC, V6, P373 19

20. Data are from the CR field of each record:
Most Cited Authors
Rank AU NC 1
ROTH WM
411 11
LEDERMAN NG
230 2
*NAT RES COUNC
397 12
BANDURA A
226 3
DRIVER R
395 13
VOSNIADOU S
214 4
JONASSEN DH
336 14
KUHN D
213 5
MAYER RE
323 15
TABER KS
196 6
VYGOTSKY LS
259 16
OSBORNE J
195 7
TSAI CC
250 17
BROWN AL
184 8
CHI MTH
249 18
SHULMAN LS
180 9
*AM ASS ADV SCI
246 19
AIKENHEAD GS
178 10
LAVE J
242 20
TOBIN K
176
Data are from the CR field of each record:
CR ROTH WM, 1997, SCI EDUC, V6, P373 20

21. Data are from the CR field of each record:
Most Cited Journals
rank J9 DF 1
J RES SCI TEACH
4707 11
COMPUT HUM BEHAV
622 2
SCI EDUC
3368 12
LEARN INSTR 3
INT J SCI EDUC
2927 13
EDUC RES
618 4
COMPUT EDUC
1668 14
COGNITION INSTRUCT
581 5
J LEARN SCI
899 15
J EDUC COMPUT RES
562 6
J EDUC PSYCHOL
877 16
EDUC PSYCHOL
523 7
ETR&D-EDUC TECH RES
829 17
STUDIES SCI ED
468 8
REV EDUC RES
825 18
RES SCI EDUC
446 9
J COMPUT ASSIST LEAR
737 19
J CHEM EDUC
443 10
BRIT J EDUC TECHNOL
717 20
INSTR SCI
433
Data are from the CR field of each record:
CR ROTH WM, 1997, SCI EDUC, V6, P373 21

22. Topic Clustering Procedure
Indexing Construction (for fast analysis)
Similarity Computation
Document Clustering
Cluster Labels Generation
Multi-Stage Clustering for Topic Trees
Multi-Dementional Scaling (MDS) for Topic Map
Cross tabulations for topic and other data 22

23. Indexing Building Bibliographic Coupling (BC) : Co-word Analysis (CW)
Constructing BC matrix
Normalize citation counts
Co-word Analysis (CW)
Remove stop words (the, of, for, on, and, at, …)
Normalize terms (stemming, lemmatization, vocabulary control)
Keyterm extraction ( patented [Tseng, 2002, JASIST])
Building inverted files for later fast computation 23

24. Similarity Computation
Doc A B
詞彙 1
詞彙 2
詞彙 T
Co-word
文獻 1
文獻 2
文獻 M
Bibliographic Coupling
T=2529 for 318 EEPA papers
M=9957
for 318 EEPA papers
Sim(A, B) =
2x|S(A)∩S(B)|
|S(A)|+|S(B)| D1 D2 Dn D1 D2 Dn 24

25. Topic Tree Agglomerative Hierarchical Clustering (AHC)
Complete link criterion
Dendrogram
D D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15 D16 D17
Threshold: 0.075
Result: 6 clusters
0.3
0.2
0.1
0.0 25

26. 主題樹範例 (電影新聞資料) 1(7): 161 : 7 Docs. : 0.3478 (美國: 9.4)
13 : : :納尼亞傳奇 美國片
55 : : :V怪客 美國片
48 : : :北國性騷擾 美國片
1 : : :惡狼ID 美國片
32 : 3 Docs. : (影迷: 7.0, 美國: 2.4)
14 : 2 Docs. : (影迷: 4.0, 絕命終結站: 3.5, 絕命: 3.5, 飛車: 2.8, 雲霄飛車: 2.8)
11 : : :奪魂鋸2美國片
27 : : :絕命終結站3雲霄飛車驚魂
16 : : :偷穿高跟鞋　美國片
9(3): 28 : 3 Docs. : (傑克: 10.0, 李安: 8.9, 傑克基倫霍: 7.0, 基倫霍: 7.0, 希斯萊傑: 3.2)
17 : 2 Docs. : (李安: 11.0, 傑克: 5.7, 斷背山: 4.9, 希斯萊傑: 4.0, 傑克基倫霍: 3.2)
3 : : :李安靠 斷背山重拾熱情
7 : : :斷背山 美國片
21 : : :鍋蓋頭 美國片
12(3): 74 : 3 Docs. : (奶油: 7.3, 絕配: 6.0, 料理: 5.1, 凱特: 4.9, 尼克: 3.2)
58 : 2 Docs. : (番紅花: 6.3, 凱特: 6.0, 番紅花醬汁: 4.9, 尼克: 4.0, 鮮奶: 4.0)
68 : : :料理絕配 跟著男主角做義國菜
71 : : :料理絕配 跟著女主角做法國菜
69 : : :料理絕配 看電影學用餐禮儀
相似度
類別序號 與篇數
類別標題詞
類別編號
(下一階使用) 與篇數 26

27. Generation of Cluster Labels
Automatic extracting cluster terms as cluster labels
Yuen-Hsien Tseng, " Generic Title Labeling for Clustered Documents", Expert Systems With Applications, Vol. 37, No. 3, 15 March 2010, pp 27

28. Multi-Stage Clustering
Each stage is an AHC
Topics
Stage 2
Concepts
Stage 1
Docs.
Outliers: below threshold, unable to be clustered 28

29. Topic Map MDS (Multi-Dimensional Scaling)
Projecting high dimensional similarities into 2 dimension similarity map for ease of visualization
6. Biomedicine
1.Chemistry
5. Material
3. Generality
2. Electronics and
Semi-conductors
4. Communication
and computers
Topic map of US patents of MOST 29

30. Topic Map and Topic Tree
Carbon Nanotube patents 30

31. Analysis by Bibliographic Coupling
Command example:
C:\CATAR\src>perl -s automc.pl -OBC SE ..\Source_Data\SE\SE.mdb
Results:
C:\CATAR\Result\SE_BC
*.html: topic tree
*all*.html: cross tabulation of topics
*.xls: cross tabulation of topics
*titles*.html: titles of each cluster 31

32. Analysis based Co-Word
Command example:
C:\CATAR\src>perl -s automc.pl -OCW SE ..\Source_Data\SE\SE.mdb
Results:
C:\CATAR\Result\SE_CW
*.html: topic tree
*all*.html: cross tabulation of topics
*.xls: cross tabulation of topics
*titles*.html: titles of each cluster 32

33. BC Analysis Example Reasonable: 100%
Thresuld=0.0
1(6): 34 : 6 Docs. : (cluster: 5.1, map: 3.0, min: 3.0, text: 2.1)
12 : 4 Docs. : (cluster: 7.0, patent: 5.2, text: 3.7, generic: 2.6, title: 2.6)
5 : 3 Docs. : (cluster: 5.0, generic: 3.1, title: 3.1, text: 2.4, document: 2.3)
1 : 2 Docs. : (generic: 4.0, title: 4.0, cluster: 3.2, document: 3.1, correlation coefficient: 2.0)
2 : ISI: : 2006:Toward generic title generation for clustered documents 6 : ISI: : 2010:Generic title labeling for clustered documents
3 : ISI: : 2007:Text mining techniques for patent analysis
4 : ISI: : 2007:Patent surrogate extraction and evaluation in the context of patent mapping
18 : 2 Docs. : (education: 4.0, content analysi: 2.0, content: 2.0, media: 2.0)
7 : ISI: : 2010:Mining concept maps from news stories for measuring civic scientific literacy in media
8 : ISI: : 2010:Trends of Science Education Research: An Automatic Content Analysis
2(3): 15 : 3 Docs. : (neural network: 3.1, quadratic: 2.3, sort: 2.3, perceptron: 1.7)
2 : 2 Docs. : (quadratic: 3.0, sort: 3.0, perceptron: 2.3, winner-take-all: 1.4, constant-time: 1.4)
13 : ISI:A1995QT : 1995:ON A CONSTANT-TIME, LOW-COMPLEXITY WINNER-TAKE-ALL NEURAL-NETWORK
9 : ISI:A1992HU : 1992:SOLVING SORTING AND RELATED PROBLEMS BY QUADRATIC PERCEPTRONS
10 : ISI:A1992HY : 1992:CONSTRUCTING ASSOCIATIVE MEMORIES USING HIGH-ORDER NEURAL NETWORKS
3(2): 14 : 2 Docs. : (automatic: 3.1, chinese: 1.4, text: 1.4, thesauru: 1.4)
0 : ISI: : 2001:Automatic cataloguing and searching for retrospective data by use of OCR text
1 : ISI: : 2002:Automatic thesaurus generation for Chinese documents
4(2): 3 : 2 Docs. : (code: 4.0, decoder: 1.4, fast: 1.4, reed-muller: 1.4)
11 : ISI:A1993MA : 1993:DECODING REED-MULLER CODES BY MULTILAYER PERCEPTRONS
12 : ISI:A1993MA : 1993:FAST NEURAL DECODERS FOR SOME CYCLIC CODES
5(1): 36 : 1 Docs. : 0 (hot: 2.0, detect: 2.0, comparison: 2.0, topic: 1.1, scientometric: 0.7)
5 : ISI: : 2009:A comparison of methods for detecting hot topics
Reasonable: 100% 33

34. BC Analysis Example: 2nd Stage
Thresuld=0.0
Reasonable: 100%
1(2): 1 : 5 Docs. : (neural: 4.0, perceptron: 3.0, code: 2.4, decoder: 1.8, network: 1.8)
1 : 15 : 3 Docs. : (neural network: 3.1, quadratic: 2.3, sort: 2.3, perceptron: 1.7)
3 : 3 : 2 Docs. : (code: 4.0, decoder: 1.4, fast: 1.4, reed-muller: 1.4)
2(2): 2 : 8 Docs. : (automatic: 5.0, document: 4.0, text: 4.0, generation: 3.0, cluster: 1.8)
0 : 34 : 6 Docs. : (cluster: 5.1, map: 3.0, min: 3.0, text: 2.1)
2 : 14 : 2 Docs. : (automatic: 3.1, chinese: 1.4, text: 1.4, thesauru: 1.4)
3(1): 4 : 1 Docs. : 0 (hot: 2.0, detect: 2.0, comparison: 2.0, topic: 2.0, scientometric: 1.0)
4 : 36 : 1 Docs. : 0(hot: 2.0, detect: 2.0, comparison: 2.0, topic: 1.1, scientometric: 0.7)
Labe ID from stage 1 34

35. BC Analysis Example: 2nd Stage35

36. Co-Word Analysis Example
Reasonable: 60%-80%
1(5): 29 : 5 Docs. : (term: 19.0, document: 6.7, algorithm: 4.0)
7 : 3 Docs. : (document: 12.2, generic: 7.7, cluster: 7.6, term: 7.4, algorithm: 6.0)
2 : 2 Docs. : (cluster: 10.8, generic: 10.0, label: 7.0, title: 7.0, document: 5.6)
2 : ISI: : 2010:Generic title labeling for clustered documents
6 : ISI: : 2006:Toward generic title generation for clustered documents
7 : ISI: : 2002:Automatic thesaurus generation for Chinese documents
3 : 2 Docs. : (map: 7.7, patent: 5.4, term: 4.1, scientific: 4.0, new: 4.0)
1 : ISI: : 2010:Mining concept maps from news stories for measuring civic scientific literacy in media
4 : ISI: : 2007:Patent surrogate extraction and evaluation in the context of patent mapping
2(3): 19 : 3 Docs. : (automatic: 7.3, text: 6.9, analysi: 4.9, approach: 4.6, topic: 1.9)
4 : 2 Docs. : (science: 7.4, analysi: 6.9, education: 5.4, science education: 5.4, research: 5.4)
0 : ISI: : 2010:Trends of Science Education Research: An Automatic Content Analysis
5 : ISI: : 2007:Text mining techniques for patent analysis
8 : ISI: : 2001:Automatic cataloguing and searching for retrospective data by use of OCR text
3(2): 1 : 2 Docs. : 1.00 (network: 7.7, memory: 4.0, associative memory: 2.7, winner-take-all: 2.0)
12 : ISI:A1992HY : 1992:CONSTRUCTING ASSOCIATIVE MEMORIES USING HIGH-ORDER NEURAL NETWORKS
9 : ISI:A1995QT : 1995:ON A CONSTANT-TIME, LOW-COMPLEXITY WINNER-TAKE-ALL NEURAL-NETWORK
4(1): 30 : 1 Docs. : 0 (trend: 6.7, different: 5.0, better: 3.0, trend observation: 3.0, choice: 3.0)
3 : ISI: : 2009:A comparison of methods for detecting hot topics
Have common term: Map, Mapping, but different interpretation 36

37. Breakdown Trends of ICT in Edu.
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
68 : 993
104 : 464
22 : 237
85 : 139
97 : 55
51 : 83
1990 38 1 9 7
1991 53 8 2 6
1992 55 4 11
1993 50 3
1994 42 18 5
1995 17 23
1996 47 12 19
1997 57 27 10
1998 66 29
1999 52 28 14
2000 69 33 15
2001 43 13
2002 44
2003 34
2004 56 59
2005 71 21
2006 37
2007 78 22 25
Main stream topic
Dying out topics
Hot topics during that period
Topic with periodic attraction
Promising topics (not yet mature)

38. Interpretation (1/2) The most valuable part of your analysis
Access file: data after parsing or loading
Updatable for BC/CW analysis
Excel file: various cross tabulation for analysis
HTML file: topic tree
Results are under C:\CATAR\Result\ :
The topic trees in stage n are under the folder name ended with Sn
The topic maps of stage n are in the folder name ended with S(n+1) 38

39. Interpretation (1/2)
Accept default parameters and try different parameters
Interpretable is more important than reasonable
Meaningful information may scatter in the results of different stages
Need domain experts to help interpretation and verification
Reference:
Chao-Mei Chen (2010) How to choose parameter in CiteSpace: 39

40. Case Studies
Yulan Yuan, Ulrike Gretzel, and Yuen-Hsien Tseng*, "Revealing the Nature of Contemporary Tourism Research: Extracting Common Subject Areas through Bibliographic Coupling ", International Journal of Tourism Research, Vol. 17, No. 5, pp. 417–431, Sep./Oct. 2015, DOI: /jtr.2004.
Yuen-Hsien Tseng*, Chun-Yen Chang, M. Shane Tutwiler, Ming-Chao Lin, and James Barufaldi, " A Scientometric Analysis of the Effectiveness of Taiwan's Educational Research Projects", Scientometrics, Vol. 95, No. 3, pp , June 2013.
Yuen-Hsien Tseng and Ming-Yueh Tsay, " Journal clustering of Library and Information Science for subfield delineation using the bibliometric analysis toolkit: CATAR", Scientometrics, Vol. 95, No. 2, pp , May 2013.
Yueh-Hsia Chang, Chun-Yen Chang, Yuen-Hsien Tseng, "Trends of Science Education Research: An Automatic Content Analysis", Journal of Science Education and Technology, Vol. 19, No. 4, 2010, pp 40

41. Remarks Start from the overview analysis For non-WoS data
So that the Wos can be parse into the database for later use
Followed by BC and CW analysis
For non-WoS data
Refer to:
C:\CATAR\Source_Data\movie\movie.mdb
C:\CATAR\Source_Data\eport\eport.mdb
Put your own data into table TPaper in the database based on the meaning of the field names
Separate each value by “; “ if multiple values in a field:
Chang, YH; Chang, CY; Tseng, YH 41