AutoMap is a text mining tool that enables the extraction of network data from texts. AutoMap can extract content analytic data (words and frequencies), semantic networks, and meta-networks from unstructured texts developed by CASOS at Carnegie Mellon.
Pre-processors for handling pdf’s and other text formats exist. Post-processors for linking to gazateers and belief inference also exist.
The main functions of AutoMap are to extract, analyze, and compare texts in terms of concepts, themes, sentiment, semantic networks and the meta-networks extracted from the texts. AutoMap exports data in DyNetML and can be used interoperably with *ORA.
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.
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