AutoMap is a text mining tool that enables the extraction of network data from texts. AutoMap can extract three types of information:
content analytic (words and frequencies), semantic networks, and meta-networks.
AutoMap uses parts of speech tagging and proximity analysis to do computer-assisted Network Text Analysis (NTA). NTA encodes the links among words in a text and constructs a network of the linked words.
AutoMap subsumes classical Content Analysis by analyzing the existence, frequencies, and covariance of terms and themes.
AutoMap has been implemented in Java 1.5.0_07.
It can operate in both a front end with gui, and backend mode.
Main functionalities of AutoMap are:
- Extract, analyze and compare mental models of individuals and groups.
- Reveal structure of social and organizational systems from texts.
AutoMap also offers a variety of techniques for pre-processing Natural Language:
- Named-Entity Recognition
- Stemming (Porter, KStem)
- Collocation (Bigram) Detection
- Extraction routines for dates, events, parts of speech
- Deletion
- Thesaurus development and application
- Flexible ontology usage
- Parts of Speech Tagging
The employed algorithm for map analysis is based on Carley's approach to coding texts as cognitive maps and Danowski's approach for proximity analysis.
Automap is also a part of the CASOS Summer Institute. At the CASOS Summer Institute, CASOS Ph.D. students have the chance to display and discuss their projects and work. The 2008 CASOS Summer Institute poster for Automap is:
"From Texts to Networks"