Group items tagged

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. 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

Another technique that Twine uses is graph analysis. This idea, explains Spivack, is similar to the thinking behind the "social graph" that Mark Zuckerberg, the founder of Facebook, extols: connections between people exist in the real world, and online social-networking tools simply collect those connections and make them visible. In the same way, Spivack says, Twine helps make the connections between people and their information more accessible. When data is tagged, it essentially becomes a node in a network. The connections that each node has to other nodes (which could be other data, people, places, organizations, projects, events, et cetera) depend on their tags and the statistical relevance they have to the tags of other nodes. This is how Twine determines relevance when a person searches through his or her information. The farther away a node is, the less relevant it is to a user's search