Quick Start Tutorial of KH Coder 2: Quantitative Content Analysis or Text Mining of English Language Data
Quick Start Tutorial of KH Coder:
Quantitative Content Analysis or Text Mining
of English Language Data
This presentation is a part of tutorials for using KH Coder.
KH Coder is a free software for quantitative content
analysis or text mining. It is also utilized for
Details and downloads:
Table of Contents
Configure KH Coder for English speaking people / English data
1. Change the interface language to English
2. Settings for analyzing English text
Notes on the stopwords
Create a new project and prepare for analysis
3. Create a new project
4. Run pre-processing
Frequently appeared words and co-occurrences
5. Word frequency list
6. KWIC and collocation stats
7. Co-occurrence network of words
Methods for exploring co-occurrences of words
Characteristics of each chapter
8. Distinctive words of each chapter
9. Correspondence analysis of words and chapters
Use coding rules to count concepts
10. Search documents with coding rules
11. Cross tabulation of the codes
1. Change the Interface Language to English
Choose “English” here
and restart KH Coder.
If you prefer the Japanese interface, you may skip this step.
You may also change the interface font.
Go to [Project] [Settings] in the menubar.
2. Settings for Analyzing English Text
(1) Go to [Project] [Settings] in the menubar.
(2) Select “Lemmatization.”
(3) Click “config.”
(4) Open the “tutorial_en”
folder, then drag the file
and drop here. (Or just paste
the content of the file here)
(5) Click “OK.”(6) Click “OK.”
Notes on the Stopwords
You can specify any words as stopwords in KH Coder.
The stopwords will be given the special POS tag “OTHER.”
Words with “OTHER” tag will be excluded from analyses by default.
3. Create a New Project
(1) Go to [Project] [New] in the menubar.
(2) Click “Browse” and open the file
(3) fill in whatever
memo you like
(4) Click “OK.”
In this tutorial we analyze a
novel “Botchan” by Soseki.
“botchan_en.txt” contains all 11
chapters of the novel.
Chapter headings are marked
with h1 tag
Next time you start KH Coder,
go to [Project] [Open] in the
menubar and open the project
you have created here.
4. Run Pre-Processing
Go to [Pre-Processing] [Run Pre-Processing]
in the menubar. Then click “OK.”
Sentence splitting, tokenization, POS tagging
and lemmatization are performed.
The results are compiled into MySQL database
for searching and statistical analysis.
When processing data, KH Coder
“concentrates” on the job. So sometimes it
looks frozen. But it is normal when CPU or disk
5. Word Frequency List
Go to [Tools] [Words] [Frequency List] in the menubar.
These are counts of base forms / lemmas
6. KWIC and Collocation Stats 1/2
(1) Go to [Tools] [Words] [KWIC Concordance] in the menubar.
(2) Input a base form of a word
and hit “Enter” on the keybord
When you change sort options,
click “Search” button again.
Double click any line to view
wider contexts. You can
change viewing Units below.
(3) Click “Stats” to open
the collocation stats.
6. KWIC and Collocation Stats 2/2
(1) Follow the steps in the previous slide to open the collocation stats.
(2) You can filter words
by POS tags.
“L1” stands for “Left 1.” Numbers in this column
indicate how many times each words appeared
just before the Node Word (left side, distance 1).
7. Co-Occurrence Network of Words
(3) Click “Config” and check “Larger nodes
for higher frequency words”, then lick “OK.”
Now you can see a co-occurrence network of high frequency words in the text.
The color change from blue (low) to pink (high). It indicates the centrality index.
(1) Go to [Tools] [Words] [Co-Occurrence Network] in the menubar.
(2) Select “Paragraphs” as Unit, then click “OK”
(4) Click “Config” and increase “edges” (co-
occurences) to “top 100,” then lick “OK.”
(5) Select “Community: modularity” as “color.”
Which version did you like?
Methods for Exploring Co-Occurrences of Words
To explore co-occurrences of words, you can also use:
hierarchical cluster analysis
co-occurrence network cluster analysis MDS
By interpreting these result, you may find major themes of the text
from groups of words which tend to appear together.
KH Coder uses R as back end to execute these multivariate methods.
8. Distinctive Words of Each Chapter
(2) Click “Heading 1.”
Top 10 distinctive words of each chapter
are tabulated. The “distinctiveness” is
calculated using Jaccard index.
Basically, if a word shows larger
probability of appearance in a specific
chapter, It’s considered distinctive.
(1) Go to [Tools] [Variables & Headings] [List] in the menubar.
(3) Select “Sentences.”
(4) Select “catalogue: Excel.”
9. Correspondence Analysis of Words and Chapters
(2) Click “OK”
Using correspondence analysis,
you can visually interpret
characteristics of each chapter.
(1) Go to [Tools] [Words] [Correspondence Analysis] in the menubar.
(3) Click “Config”, then reduce words
to “Top 30,” check “Bubble plot,”
uncheck “Size of variables...,” and
click “OK.” (This step is optional.)
Use Coding Rules to Count Concepts
In some cases, we have to count concepts, not words.
To count concepts, you can compose “cording rules” like this:
store or shop or ( merchandise and not develop )
Indicates the name of this code.
The conditions for attaching this code. Cases that contain words
like store and shop are given the code “shopping.” The
parenthetical notation means that cases should contain the word
“merchandise” but should not contain the word “develop.”
If a case is acceptable under multiple coding rules, multiple codes will
be given to the case.
We use “tutorial_en/themes.txt”
as example coding rules in this
tutorial. Please open this file and
check the content.
10. Search Documents with Coding Rules
(1) Go to [Tools] [Documents] [Search Documents] in the menubar.
(2) Click “Browse” and select
(3) Select “Paragraphs”
(4) Double click a code
(5) Double click a result to
view the whole paragraph. When you compose a coding
rule, it is important to search and
check the actual documents
which are acceptable under the
11. Cross Tabulation of Codes
(1) Go to [Tools] [Coding] [Crosstab] in the menubar.
(2) Click “Browse” and select
(3) Select “Sentences”
(5) Click “all” to
make a graph.
In the latter half of the novel,
it looks like “aggression”
overwhelms “positive affect”
and forms the climax of the
story at chapter X.
(4) Click “Run”
I am grateful to students who attended the 2011
“text mining” class at Doshisha University (Faculty
of Culture and Information Science) for giving me
some hints on composing coding rules for
Questions or Comments?
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