AutoMap©
1. General Information
2. Product
3. Key Terms and Constraints
4. Coding Choices: Filtering
the Text and Windowing
5. References
1. General Information
AutoMap extracts, analyzes and represents cognitive maps of texts as representations
of individual's mental models. Mental models are conceptual networks of relations
between concepts. Texts contain a portion of the author's mental model.
Differences in the distribution of concepts and relationships among the Concepts
across texts provide insight into the similarities and differences in the
content and structure of texts.
AutoMap contains content analytic techniques and map analytic techniques
to code and analyze texts and a toolkit for data reduction. The content analytic
techniques are focused on collecting and analyzing quantitative information
on the concepts. The map analytic techniques extract conceptual networks
of texts.
AutoMap is not restricted to any language.
AutoMap is not restricted to any language.
2. Product
AutoMap is a program written in Java, that runs under the Linux, UNIX and
Windows operating systems. The release that has been made available as AutoMap.exe
runs only under Windows.
As an input AutoMap takes one or several texts. The user can pre-process
the text and determine the window size and further settings according
to the research question. Every step of data reduction is visualized and
can be stored for further analysis. As an output AutoMap generates
the pre-processed text, a coded map, a visualization of the map and a statistical
overview.
There are no scientific standards for defining information as irrelevant
or how to create a delete list or a thesaurus. The user has to determine
the most appropriate level of generalization considering his research question.
AutoMap supports this decision making process.
3. Key Terms and Constraints
1. A concepts is a single idea or ideational kernel
represented by a single word or phrase.
2. A relation is a connection between two concepts.
3. A statement is a set of two concepts and the
relation between them.
4. A map is a network of concepts formed from statements.
Two statements are linked if they share one concept.
5. A Thesaurus is a set of key concepts and their
synonyms.
6. A key concept is a concept that other concepts
will be translated into.
4. Coding Choices: Filtering the Text and Windowing
Coding a map is a two-step process that requires the user to make decisions
about Filtering
a text and Windowing
(proximity analysis).
The coding choices may change the analysis results significantly.
Filtering a text means to reduce the data to a minimized set of the most
relevant, content-bearing terms. Pre-processing is a semi-automated, iterative
process that allows the user to stay close to the data and to beyond explicitly
articulated ideas to implied ideas. The size of both text and map is decreased
significantly and therefore meaningful comparisons across texts becomes possible.A
simplified text is generated that can be visually inspected. There are no
scientific standards for defining information as irrelevant. The user has
to determine the most appropriate level of data reduction considering his
research question.
AutoMap allows the researcher to use a three step process for data reduction:
punctuation, deletion and generalization. Inter-coder reliability may be
a second criteria.
AutoMap helps to make decisions and to realize the generalization.
By determining the punctuation the user decides whether statements within
sentences, paragraphs or the entire text are considered in the analysis.
On a more detailed level deletion
and generalization
are applied to filter the text.
Windowing is a method that codes the (filtered) text as a map by
putting relationships between pairs of Concepts that occur within a window.
A window is a set of contiguous concepts. By determining the window size
the user defines how proximally distant concepts can be from each other and
still have a relationship.
Delete List:
Deletion removes words from the text which do not help answer the research
question such as proper names, pronouns, conjunctions, articles, prepositions
and notations.
AutoMap has two delete lists available – an extensive one and a limited one
– and the researcher can modify these or design a unique one.
To view these Delete Lists, go to the Delete Concepts Index Card and select
one of the Lists.
To construct an optimal Delete List the user should apply a predefined set
of concepts and modify this List during the process of coding.
AutoMap supports this process.
The user can choose one of the predefined Delete Lists.
After running a first analysis, the Map-output is to be checked to decide
which further concepts should be deleted.
Then the Delete List is to be extended by these concepts.
The intermediate versions of the filtered text are displayed on the Concepts
deleted Index card and can be saved.
The process can be repeated as often as necessary.
Thesaurus:
A Thesaurus is built up by synonyms and key concepts. The synonyms will be
translated into the key concepts. A Thesaurus is typically designed specifically
for a dataset. AutoMap uses the entries in the thesaurus to search the text
and “translate” specific words and phrases into more basic concepts specified
by the researcher.
The user can decide if he wants to keep the rest of the text after replacing
concpets or not.
To generate an optimal Thesaurus the user should apply a first draft of his
Thesaurus and modify it during the process of coding.
AutoMap supports this process.
After running a first analysis, the Map-output can be checked to decide which
further concepts should be replaced.
Then the Thesaurus can be modified.
The intermediate versions of the filtered text are displayed on the Concepts
replaced Index card and can be saved.
The process can be repeated as often as necessary.
Once the user has defined a Thesaurus, AutoMap offers two ways to apply it:
When the words and phrases included in the thesaurus are replaced by the corresponding
key concepts, the rest of the text can be maintained or neglected. The difference
between is the resulting data reduction, that is much higher if the rest
of the text, which is not included in the thesaurus, will be neglected.
When the pre-processed text will be analyzed in order to code a map, statements
will be placed between the concepts within every single window.
If the user did not apply a delete or a thesaurus, statements will be placed
between all contiguous concepts.
If a delete list was applied and/ or a thesaurus in the way that concepts
were replaced by corresponding key concepts and maintaining the rest of the
texts, statements will be placed between the concepts of the pre-processed
text.
If the user applied the thesaurus in the way that concepts were replaced
by corresponding key concepts and the rest of the text was neglected, AutoMap
offers two methods to place statements between concepts: direct adjacency
and rhetorical adjacency.
Direct adjacency means that only the key concepts are maintained while
all the rest of the text will not be considered. AutoMap displays the text
that is to be analyzed as the resulting plain string of key concepts.
Rhetorical adjacency means that the original distance between the
key concepts will be considered for the analysis. In this approach AutoMap
displays the resulting text as a string of xxx symbols and key concepts.
The xxx symbols can be considered space holder the by.
Examples for the different arroaches to apply the thesaurus are provided
in the AutoMap Help under the root directory of AutoMap.
Windowing
Windowing is a method that codes the (filtered) text as a map by putting
relationships between pairs of Concepts that occur within a window.
A window is a set of contiguous concepts.
By determining the window size the user defines how proximally distant concepts
can be from each other and still have a relationship.
AutoMap offers Windowing as a completely automated process.
The user can select a window size between 2 and 100
Windowing may include two dangers:
Overlinking: Put relationships between
concepts that are not semantically related but simple are contiguous
Underlinking: Not put relationships between concepts
that are semantically related but are distant from each other
due to grammatical phrasing.
5. References
For further Information about Textual Analysis see:
http://www.hss.cmu.edu/departments/sds/faculty/carley/publications.htm
All listed abstracts are available under this direction.
For full versions of texts please contact Prof. Kathleen Carley:
carley+@andrew.cmu.edu
Kathleen M. Carley, 1997, "Network Text Analysis: The Network Position
of Concepts."
Chapter 4 in C. Roberts (Ed.), Text Analysis for the Social Sciences:
Methods for Drawing Statistical Inferences from Texts and Transcripts.
Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 79-100.
Kathleen M. Carley, 1997, "Extracting Team Mental Models Through Textual
Analysis."
Journal of Organizational Behavior, 18: 533-538.
Abstract
An approach, called map analysis, for extracting, analyzing and combining
representations of individual's mental models as cognitive maps is presented.
This textual analysis technique allows the researcher to extract cognitive
maps, locate similarities across maps, and combine maps to generate a team
map. Using map analysis the researcher can address questions about the nature
of team mental models and the extent to which sharing is necessary for effective
teamwork. This technique is illustrated using data drawn from a study of
software engineering teams. The impact of critical coding choices on the
resultant findings is examined. It is shown that various coding choices have
systematic effects on the complexity of the coded maps and their similarity.
consequently a thorough analysis requires analyzing the data several times
under different coding choices. For example, re-analysis under different
coding scenarios revealed that although members of successful teams tend
to have more elaborate, more widely shared maps than members of non-successful
teams, this difference is significant only when the data is unfiltered. Thus
a better interpretation of this result is that all teams have comparable
models, but successful teams are able to describe their models in more ways
than are non-successful teams.
Kathleen Carley, 1994. "Extracting Culture through Textual Analysis."
Poetics, 22:291-312.
Abstract
Language has been viewed as a window on the mind. Language is also a window
on culture. Through analyzing texts the interplay between human cognition
and culture can be examined. Through analyzing texts cognitive similarities
and differences across individuals, which serve as a basis for culture can
be described. Through analyzing texts the impact of culture on individual
behavior can be examined. In addition, such analyses can locate similarities
and differences across cultures and changes within cultures. This paper explores
the relative benefits for using content analysis and map analysis for extracting
and analyzing culture given a series of texts. content analysis has been
the traditional textual analysis method used for examining culture. However,
it is not theoretically grounded. In contrast, map analysis has received
less use and is theoretically grounded in an understanding of human cognition.
It is shown that under certain conditions map analysis subsumes content analysis.
Researchers can thus use map analysis not only to extract and analyze culture
but to examine the relationship between cognition and culture. Illustrative
applications are drawn from four different studies.
Kathleen Carley, 1993, "Coding Choices for Textual Analysis: A Comparison
of Content Analysis and Map Analysis."
In Marsden P. (Ed), Sociological Methodology , 23: 75-126. Oxford:
Blackwell.
Abstract
Content and map analysis, procedures for coding and understanding texts,
are described and contrasted. Where content analysis focuses on the extraction
of concepts from texts, map analysis focuses on the extraction of both concepts
and the relationships among them. Map analysis thus subsumes content analysis.
coding choices that must be made prior to employing content-analytic procedures
are enumerated, as are additional coding choices necessary for employing
map-analytic procedures. The discussion focuses on general issues that transcend
specific software procedures for coding texts from either a content-analytic
or map-analytic perspective.
Kathleen Carley & Michael Palmquist, 1992, "Extracting, Representing
and Analyzing Mental Models."
Social Forces , 70(3): 601-636
Abstract
When making decisions or talking to others, people use mental models of the
world to evaluate choices and frame discussions. This article describes a
methodology for representing mental models as maps, extracting these maps
from texts, and analyzing and comparing these maps. The methodology employs
a set of computer-based tools to analyze written and spoken texts. these
tools support textual comparison both in terms of what concepts are present
and in terms of what structures of information are present. The methodology
supports both qualitative and quantitative comparisons of the resulting representations.
This approach is illustrated using data drawn from a larger study of students
learning to write where it is possible to compare mental models of the students
with those of the instructor.
Eleanor T. Lewis & Jana Diesner & Kathleen Carley, 2001, "Using
Automated Text Analysis to Study Self-Presentation Strategies"
Presented at the Computational Analysis of Social and Organizational Systems
(CASOS) conference, Pittsburgh Pennsylvania, July 2001. Available through
the CASOS working paper series.
Abstract
Extracting and representing the networks of ties between concepts in a set
of texts creates a “map” of each text. Map analysis allows a researcher to
compare the networks of ties between concepts in these texts by systematically
reducing their content. The goals of this research paper are to answer both
a methodological and a substantive question. First, how do the choices a
researcher makes about how to generate maps using an automated text program
alter the results, and how do these results compare to the results of hand-coding?
Second, how can we interpret the results of map analysis to better understand
the strategies authors use to manage their self-presentation, a central purpose
of many texts. The texts we use are a subsample of a dataset of applications
by entrepreneurs for an “Entrepreneur of the Year” award. Applicants value
uniqueness in their application’s content because it sets them apart and
demonstrates their worthiness for the award, but the value placed on uniqueness
in the structure of their accounts is not as clear. Our analysis allows us
to extract four general self-presentation strategies: the prepared entrepreneur,
the driven entrepreneur, the creative niche entrepreneur, and the humble
entrepreneur (a single entrepreneur may employ multiple strategies).
Contact:
Prof. Kathleen Carley
Carnegie Mellon University, Pittsburgh, PA
E-Mail: Carley+@andrew.cmu.edu
Jana Diesner
Carnegie Mellon University, Pittsburgh, PA
janadiesner@gmx.net