ITMS – Interactive Text Mining Suite ITMS is a web application for text analysis. This application offers the computational and statistical power of R and the Shiny web application interactivity.
The new release includes the following features:
- Import Zotero rdf files, Google Book API, json and xml
- Dynamic preprocessing steps
- Stemming in multiple languages
- Tuning parameters for cluster classification
- Word cloud comparison
- Word cloud customization
- Metadata extraction
Contributors: Jefferson Davis, Irina Trapido and Jay Lee
As always, please do not hesitate to contact if you have any issues or to request new features!
We just added a new feature to Interactive Text Mining Tool: Word and Sentence Length.
In Data Visualization Panel, select Word Frequency Tab and click on Length. You can select a specific document and explore its content.
If you are interested in Punctuation Visualization (for more information, read Adam Calhoun’s Blog), select Punctuation Analysis Tab and click Punctuation.
Interactive Text Mining Suite v.1.2 allows for more interactive data pre-processing.
ITMS uses qdapRegex, tm and RTextTools packages for data pre-processing. Feedback, suggestions and bug reports are greatly appreciated.
Our Language Variation Suite and Interactive Text-Mining Tool are now accessible via SmartPhone and iPad.
Use QR Scanner App to scan the following QR codes, open them in your browser on Smart Phone or iPad. Make sure you have your files (csv or text files) in your Dropbox. Navigate to the Descriptive Statistics in LVS or Data Preparation in ITMS, select choose files and upload them.
We always welcome any suggestions, feedback and bug reports!
ITMS integrates visual and statistical R with an interactive Shiny application to examine unstructured data (aka text documents). At present, ITMS provides several text-mining analyses for scholarly articles and literary texts (e.g. topic, frequency and cluster analyses).
ITMS is an ongoing project by interdisciplinary team of researchers from Indiana University (Olga Scrivner and Jefferson Davis). We are also developing an NEH proposal to advance this research.
Your feedback and suggestions as well as bug reports will be very appreciated (obscrivn AT indiana PERIOD edu).