How the General Inquirer is
and a comparison of General Inquirer
Because overlaps exist among content-analysis tools as well as with
"qualitative analysis", text-management, "natural language
processing" and some artificial-intelligence software, it is important to
have realistic expectations about what each content-analysis tool, including
the General Inquirer, can readily provide. For some research projects,
especially those involving intensive analyses of modest quantities of text,
other text-analysis software may be much more appropriate.
The General Inquirer is basically a mapping tool. It maps each text file
with counts on dictionary-supplied categories. The currently distributed version combines the "Harvard
IV-4" dictionary content-analysis categories, the "Lasswell" dictionary
content-analysis categories, and five categories based on the social cognition
work of Semin and Fiedler, making for 182 categories in all. Each category is a list of words and
word senses. A category such as "self references" may contain only a
dozen entries, mostly pronouns. Currently, the category "negative"
is our largest with 2291 entries.
Users can also add additional categories of any size.
In order to map category assignments with reasonable accuracy, the General
Inquirer software spends most of its processing time identifying commonly used
word senses. For example, it distinguishes between "race" as a
contest, "race" as moving rapidly, "race" as a group of
people of common descent, and "race" in the idiom "rat
race". The General Inquirer also cautiously removes common regular
suffixes so that one entry in a category can match several inflected word
forms. A category entry can be an inflected word (for example,
"swimming"), a root word ("swim" would match
"swimming", if "swimming" is not a separate entry) or a
word sense (for example, "swim#1") identified by our disambiguation
routines of an inflected or root word form. These English stemming procedures,
integrated with English dictionaries and routines for disambiguating English
word senses, limit the current Inquirer system to English text
Even though these disambiguation routines often require the Inquirer
to make several passes through a sentence, the Inquirer is designed to
process large amounts of text in a reasonable amount of time. Text files
are grouped for processing into folders (compatible with input formats
used by some other systems such as LIWC). The output is a matrix of "tag
counts" for each category, with separate rows of counts for each file
processed. Depending on the software and computer system used, as well
as the length of each file, the Inquirer can assign counts for all 182
categories to text files at the rate of about a million words of text per
hour. Some Inquirer projects indeed have involved analyzing several
million of words of text. As with several other content-analysis systems
such as LIWC, the Inquirer is especially suited for projects that draw on
large amounts of text from the "information superhighway" where
there are more documents being studied and/or larger amounts of text than
can be coded either completely manually or with a computer-assisted
manual procedure for assigning codes. The main output from the Inquirer
can be displayed as an Excel spreadsheet, with both raw frequency counts
and indexes scaled for document length. Statistical tests then evaluate
whether there are statistically reliable differences between the texts or
groupings of texts being studied.
Text-analysis tools span a wide range as to whether they just provide a
vessel for manipulating text, or whether they come with content categories and
language-specific routines. Those tools that are primarily text-processing
vessels identify words (characters bounded by spaces or punctuation) as units
and, except for problems arising from the use of special alphabets, tend to be
language-independent. The Inquirer is one of the most advanced systems
towards the other end of that range.
However, although the Inquirer comes with 182 categories, it is not like
some content-analysis software in supplying category frequency norms. As
illustrated below, the user must specify what contrasts are to be compared
statistically. These might be samples of editorials from different newspapers,
speeches from different political candidates, stockholder reports from
different companies, etc. Whether a document is high or low in a category
frequency is always relative to what contrasts are being made. However, using
the same categories across research projects facilitates making such
comparisons and contributes to cumulative research. Any new categories added
remain readily available for others to use, especially if they are then
included for distribution on this website. The five Semin and Fiedler
categories are an example of such an addition.
The 182 General Inquirer categories were developed for social-science
content-analysis research applications, not for text archiving, automatic
text routing, automatic text classifying, or other natural-language
processing objectives, although they may be relevant to some of them.
People, not computers, created these categories, although some category
developers drew upon cluster analyses produced by computers. Many
categories were initially created to represent social-science concepts of
several grand theories that were prominent at the time the system was
first developed, including those of Harold Lasswell, R.F. Bales, Talcott
Parsons and Charles Osgood. It also included categories relevant to
"middle-range" theories, such as David McClelland's theories
regarding needs for achievement, power and affiliation. With further
category revisions over the years, some of these original ties to
particular social-science theories are now somewhat diffused.
Creating a useful category for content analysis can be a considerable
undertaking. Several tools can be help, such as a desk thesaurus. An extensive
electronic lexical database such as Wordnet (developed by George Miller and
colleagues at Princeton University) may also be helpful, but it can also
overwhelm the category developer. An example of software that is being
developed to draw upon Wordnet resources is WordStat.
Clearly specifying what a category is to represent so that everyone agrees
on what entries the category should contain is not always as easy as one might
think. This task is somewhat comparable to producing a set of survey questions
that everyone agrees has validity in measuring a well-specified construct.
Agreement may be hard to obtain. Ideally, the outcome of such a debate is a
better understanding of what is being measured, as defined by a reasonably
crisp set of categories.
Normally, each entry in a category is given equal weight. However, this is
a choice of the category developer rather than any inherent limitation of the
computer. Earlier versions of some Inquirer dictionaries included categories
with weightings, but we found it difficult to obtain agreement about the
weights and they added little to the validity of our counts. Currently, none
of the categories employ weightings.
Special versions of the General Inquirer could also be used to develop
scoring procedures that enlist multiple categories and analyze the tag
patterns assigned to each sentence. This tag assignment pattern across
sentences could then be used to generate an overall assessment of a document.
For example, each sentence of a story could be assessed for imagery related to
McClelland's "need achievement" and then the pattern of assessments
across the sentences used to evaluate whether the story as a whole contained
"need achievement". Another example is the use of several category
assignment patterns to predict whether a suicide note is real or simulated.
Such scorings procedures are illustrated in Ogilvie, D.M., Stone, P.J. and
Kelly, E.F. (1982) "Computer-aided content analysis" in A
Handbook of Social Science Methods edited
by R. B Smith (Ballinger Press) as well as mentioned in some other Inquirer
publications. For some studies, overall Inquirer codings correlated with those
of expert manual coders about as well as expert coders correlated with one
another. These procedures have recently been revisited by some researchers.
Researchers who study a group of documents intensively have gained
additional insights by examining how the Inquirer counts can be used to
map each document relative to the other documents in a corpus. Mapping
tools such as "correspondence analysis" (which is used more in Europe
than the U.S. and is more appropriate than factor analysis for
distributions such as tag category frequencies) can help discern patterns
in the data. For example when we used correspondence analysis to map the
tag counts for British party manifestos over several decades, both the
changing contrasts in party differences and the direction of their
movement over time made interpretable sense.
Standard interfaces between disambiguated, tagged, text output as input to
qualitative-analysis packages are mostly yet to be developed, mainly
because of our current focus on larger databases.
Large textual data bases can often be downloaded and assembled from
Lexis/Nexis, newspaper archives, government web sites, political candidate
websites and many other resources available on the Internet in surprisingly
short order. This can be aided by programs that gather up website texts on
specified topics, such as commentaries about a product, book or film. At
Harvard, Vanja Buvac has utilized such procedures to gather commentaries
appearing on "epinions."
Comparison with inductive text-analysis tools
As a tool that maps pre-specified categories, the General Inquirer differs
from purely inductive mapping tools, such as the so-called neural-net
building procedures that are now included in several software packages.
However, as the correspondence analysis example above indicates, some
inductive tools may be applied to Inquirer-produced spreadsheets of category
counts, mapping either the relationships between categories or the
relationships between documents into a multidimensional space.
One useful comparison is between the General Inquirer and SPSS's TextSmart , an inductive clustering tool primarily designed
for open-ended survey answers. In addition to providing completely inductive
automatic clustering, TextSmart also
gives its users an option to supply categories of words that are to be treated
as synonyms in the clustering process. However, categories are not provided nor
does TextSmart attempt to
identify word senses.
TextSmart is relatively new and we have yet to see many publications based
upon its clustering procedures. In contrast, correspondence analysis, has
been applied to various raw text applications, with a book by Lebart and
colleagues (Exploring Textual Data, Kluwer, 1997) devoted to reporting them. Many
additional applications can be found by doing a "Google" on "correspondence
A very different inductive approach is "Latent Semantic Analysis", developed by
Thomas Landauer and his colleagues at the University of Colorado. Their procedures
are described fully at http://lsa.colorado.edu .
Fortunately, there need not be an either-or choice between one approach and
another. Once text is collected to be processed on computers, it can be
processed by different software, or combinations of software, at little
Comparison with artificial intelligence and natural-language processing
Unlike some artificial intelligence programs that can be applied to texts
within limited topic domains, the General Inquirer simply maps text according
to categories and does not search after meaning. General Inquirer mappings have
proven to supply useful information about a wide variety of texts. But it remains
up to the researchers, not the computer, to create knowledge and insight from
this mapped information, usually situating it in the context of additional
information about the texts' origins.
A book by Charles Jonscher, (The Evolution of Wired Life, Wiley Press, 1999) argues that younger adults,
having been using computers since they were children, tend to be more
realistically sage in their expectations of what computers do well. He says
they recognize that mapping functions (even those offered by simple
spreadsheet tools) can have considerable value, but that they do not assume
such mappings in themselves constitute knowledge or wisdom. Our experience
from teaching content analysis over several decades tends to confirm
Jonscher's observations. Much confusion existed in the early days of
large main-frame computers between content-analysis of text and understanding
text. Programs such as Weizenbaum's "Eliza" that pretended to be a
psychotherapist only added, to Weizenbaum's surprise, to the confusion. We
hope that era is behind us and we can get on with our research.
Artificial intelligence tools, natural-language processing tools, and
content-analysis tools such the General Inquirer should not be expected
to generate "meaning" in either the sense of an ordinary
listener's comprehension or that of a specialist such as a linguist,
psychoanalyst, or cultural anthropologist. That acknowledged, researchers
may also want to consider what different insights AI and NLP programs
offer. The General Inquirer already provides for each document one
rudimentary NLP index, namely the document's mean word length. Such a
simple index, in fact, often discriminates between sources, reflecting
consistent preferences for shorter or longer words. Various other indexes
offered by natural language processing software, much of it available on
the Internet, could also be enlisted, as described in Manning and
Schutze's Foundations of Statistical Natural Language
Processing (MIT Press, 1999). Such
indexes could be incorporated into a discriminate function analysis, or
they perhaps also could be fed into a neural-net program, such as
Clementine, to learn to
distinguish groups of texts.
As more textual information is gathered over the internet, we can expect more
use of resources both from artificial intelligence and natural language processing
as well as content-analysis procedures to analyze patterns in such texts, including
software that provides fairly elaborate analyses of grammar. One application
already in extensive development, for example, is the utilization of computers to
analyze GMAT essays written by applicants to business schools.
Websites comparing text-analysis software
Fortunately, content analysis, qualitative analysis, and text management
software packages are compared on several websites. We find these two web sites
to be especially useful overviews:
This German site
has been reorganized following the June 2000 International Communications
meetings that featured workshops on content analysis. Describes content
analysis tools targeted to different languages, as well as lists of software
for natural language processing. Content-analysis software is listed according
to whether or not it is dictionary based. Also discusses some of the
more frequently used category systems.
State University web site also very useful and is frequently updated with
references to articles about content analysis.
Inasmuch as the Inquirer is not commercially marketed, we recommend that
researchers who have not taken a seminar or workshop on the General Inquirer
try using commercially available content-analysis tools before contacting us
about using the Inquirer. One of the few dictionary-based content-analysis
tools to include a form of word sense disambiguation, developed by CL Research, is cited as MCAA on the German
site and as DIMAP on the Georgia State site. That software could especially
useful if your research question can be cast in terms of scores on their four
Comparison with qualitative-analysis and text-management tools.
Qualitative-analysis tools generally are especially designed for organizing
textual information. They have been found especially useful, for example, for
organizing field notes. These text-management tools search, locate and count
instances of specified occurrences in texts. Generalizations about these tools
become difficult because of overlap in what various software packages provide.
While many of them provide for user-supplied word categories, none of them, to
our knowledge, provide any word-sense identification procedures or an extensive
set of preexisting categories comparable to the General Inquirer.
Many of these tools are better geared to identifying topics in contrast to
the treatment of topics. A tool may quickly locate, for example, those
documents. that are concerned with "training and education". They are
less well suited to evaluate, for example, the document's overall level of
positivity, use of understatement, or emphasis on virtues.
Going to the qualitative research sites and then downloading their
demo programs can be helpful. In our own explorations using some of these
sites, we have found the German Atlas/ti, the Australian Nud*ist (also called QSR)
and the American HyperResearch, to be outstanding, especially for
the organization of field or observation notes. WinMax, we are told, has become especially popular in
For more straightforward text management software that includes options to
create lists of equivalence words, we particularly recommend another Australian
We recommend that students investigate and compare these different tools
through their web sites. Having a good idea of what the different systems offer
can be helpful in mapping an effective research strategy
What statistics are used in General Inquirer research?
Unlike some dictionary-based content analysis approaches such as that
offered by CL Research, the General
Inquirer does not provide norms. If about a tenth of the words in a document
were scored as positive, we would say from our experience that is quite a
lot. Also, we generally expect there to be more positive than negative words.
However, we do not have a norm that says 5.7% of the words are expected to be
positive or that the expect ratio between positive to negative words is 1.83.
We have not been comfortable about what such norms, based on a corpus of
supposedly neutral texts, really signify, especially when inferences are
drawn based upon very small differences from these norms.
Instead, General Inquirer research is comparative between the cases studied
within a research project. The General Inquirer output
spreadsheet usually has such identifiers in the first columns, taken from
the file names that were created when preparing
Most of the statistics used in General Inquirer research involve
simple one-way analysis of variance. This can be carried out in Excel
(especially with its statistical "add-in" implemented), SPSS or
JMP, as well as other spreadsheet-based software. The charts below were
prepared using JMP,
which offers several advantages, including grouping by text categories
(rather than only by numbered categories, as with SPSS) and providing the
between-group comparison displays shown below. It has a student-priced
edition, which also includes correspondence analysis, that runs on both
PC's and Macs.
These analyses of variance evaluate whether the between-group variance is
significantly greater than the within-group variance using scores scaled for
document length. The first step therefore is to define groups of documents so
there are multiple documents within each group. The within-group variance will
generally be less if each document is based upon a reasonable amount of text
and there are more documents in a group. The within-group variance is also
reduced if the cases in each group are less variegated. In the examples below,
between-group differences held up even though the cases within each group
spanned different topic areas.
The two charts below are typical examples of one-way ANOVA comparisons. They
show the percent of positive words and the percent of negative words for the
campaign-related speeches of each of the five American presidential candidates
(in the presidential primaries as of December, 1999), as gleaned from their web
sites. As can be seen in the F statistics, the between-candidate mean
differences, relative to the within-candidate variances, are significant in
both tables at the .0001 level.
The dots for each candidate show the spread of scores for his speeches. The
width of each diamond shows that candidate's proportion of the total data, with
Gore and McCain having respectively 55 and 39 speeches, considerably more than
the others. The height of each diamond indicates a 95% confidence interval. If
two diamonds do not overlap, such as those of McCain and Bradley on both
charts, they are considered significantly different. The charts show McCain is
higher on the use of both positive and negative words than, for example, Gore.
Bradley is low in the use of either positive or negative words. Bush has positive
words dominating over negative ones, while Buchanan is more negative than
% Positive Words By Candidate
Analysis of Variance
Source DF Sum
of Squares Mean Square F Ratio
Model 5 142.02875 28.4058 15.2455
Error 141 262.71392 1.8632 Prob>F
Total 146 404.74267 2.7722 <.0001
Means for Oneway Anova
Level Number Mean Std Error
bradley 18 5.93667 0.32173
buchanan 9 5.31222 0.45500
bush 11 8.74455 0.41156
forbes 15 7.25600 0.35244
gore 55 7.14582 0.18406
mccain 39 8.42282 0.21857
% Negative Words By
Analysis of Variance
Source DF Sum of Squares Mean Square F Ratio
Model 5 84.78421 16.9568 9.2348
Error 141 258.90143 1.8362 Prob>F
Total 146 343.68564 2.3540 <.0001
Means for Oneway Anova
Level Number Mean Std Error
bradley 18 3.21389 0.31939
buchanan 9 4.01333 0.45169
bush 11 3.94636 0.40857
forbes 15 3.00533 0.34987
gore 55 3.45145 0.18272
mccain 39 5.06769 0.21698
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