http://www.casos.cs.cmu.edu/projects/automap/Jana_SI08-poster-final.pdf
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.
| Source | Carnegie Mellon University / Jana Diesner / Kathleen Carley |
| Difficulty | unknown |
| Compatibility | windows, linux, Unix, Macs |
| Rating | 0 |
| Language | en |
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Automap
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