1. SEASR Analytics Loretta Auvil lauvil@illinois.edu Automated Learning Group Data-Intensive Technologies and Applications, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

2. SEASR Overview

3. SEASR Focus Project’s focus: –Supporting framework –Developing –Integrating –Deploying –Sustaining a set of Reusable and Expandable software components and SEASR can provide benefit a broad set of data mining applications for scholars in humanities

4. SEASR Goals The key goals are: –Support the development of a state-of-the-art software environment for unstructured data management and analysis of digital libraries, repositories and archives –Develop user interfaces, a data-flow engine and the data-flows that data management, analysis and visualization –Support education and training through workshops to promote its usage among scholars

5. Workshop Objective The objective of the workshop is to: Introduction of SEASR Learn what analytics SEASR can do

6. The SEASR Picture

7. SEASR Enables Scholarly Research Discovery –What are the words used in the corpus? –What named entities (people, locations, dates) can be extracted? –What hypothesis or rules can be generated by the “features” of the corpus? –What “features” or language of the corpus best describes the corpus? –What are the “similarities” between elements, documents, or corpuses to each other? –What patterns can be identified?

8. Enables Scholar to Ask… Pattern identification using automated learning –Which patterns are characteristic of the English language? –Which patterns are characteristic of a particular author, work, topic, or time? –Which patterns based on words, phrases, sentences, etc. can be extracted from literary bodies? –Which patterns are identified based on grammar or plot constructs? –When are correlated patterns meaningful? –Can they be categorized based on specific criteria? –Can an author’s intent be identified given an extracted pattern?

9. Tag Cloud Counts tokens Several different filtering options supported

10. Flesch-Kincaid Readability Test Results show scores for each item selected –Designed to indicate comprehension difficulty when reading a passage of contemporary academic English –Flesch Reading Ease: higher scores indicate material that is easier to read; lower numbers mark passages that are more difficult to read –Flesch–Kincaid Grade Level: result is a number that corresponds with a grade level

11. Dunning Loglikelihood Feature comparison of tokens Specify an analysis document/collection Specify a reference document/collection Perform Statistics comparison using Dunning Loglikelihood Example showing over-represented Analysis Set: The Project Gutenberg EBook of A Tale of Two Cities, by Charles Dickens Reference Set: The Project Gutenberg EBook of Great Expectations, by Charles Dickens Example showing over-represented Analysis Set: The Project Gutenberg EBook of A Tale of Two Cities, by Charles Dickens Reference Set: The Project Gutenberg EBook of Great Expectations, by Charles Dickens

12. Date Entities to Simile Timeline Entity Extraction with OpenNLP Dates viewed on Simile Timeline

13. Frequent Patterns Given: Set of documents Find Frequent Patterns such that –Common words patterns used in the collection Evaluation: What Is Good Patterns? Results: 1060 patterns discovered 322: Lincoln 147: Abe 117: man 100: Mr. 100: time 98: Lincoln Abe 91: father 85: Lincoln Mr. 85: Lincoln man 75: day 70: Abraham 70: President 68: boy 67: Lincoln time 65: Lincoln Abraham 65: life 63: Lincoln father 57: men 57: work 52: Lincoln day … 322: Lincoln 147: Abe 117: man 100: Mr. 100: time 98: Lincoln Abe 91: father 85: Lincoln Mr. 85: Lincoln man 75: day 70: Abraham 70: President 68: boy 67: Lincoln time 65: Lincoln Abraham 65: life 63: Lincoln father 57: men 57: work 52: Lincoln day …

14. HITS Summarizer Find the top sentences and tokens from all items submitted

15. Text Clustering Clustering of Text by token counts Filtering options for stop words, Part of Speech Dendogram Visualization

16. NEMA: Executes a SEASR flow for each run –Loads audio data –Extracts features for every 10 sec moving window of audio –Loads and applies the models –Sends results back to the WebUI NESTER: Annotation of Audio via Spectral Analysis Audio Analysis

17. Emotion Tracking Goal is to have this type of Visualization to track emotions across a text document (Leveraging flare.prefuse.org)

18. Future: Application for Meme “MemeTracker builds maps of the daily news cycle by analyzing around 900,000 news stories and blog posts per day from 1 million online sources, ranging from mass media to personal blogs”

19. Where can I Run SEASR Analysis Services that can be executed from –SEASR website –Zotero –MONK –VUE

20. SEASR Community Hub Explore existing flows to find others of interest –Keyword Cloud –Connections Find related flows Execute flow Comments

21. What is Zotero? (from Zotero Quick Start Guide) A citation manager. It is designed to store, manage, and cite bibliographic references, such as books and articles. In Zotero, each of these references constitutes an item. An extension for the Firefox web-browser by the Center for History and New Media at George Mason University. Installed by visiting zotero.org and clicking the download button on the page.

22. SEASR Analytics for Zotero An extension for the Firefox web-browser by the SEASR Team Uses your Zotero Collections Performs analysis using SEASR Services

23. The Value Add for SEASR & Zotero Analytical Results are saved as Zotero items (View Snapshot) –Includes metadata –Item naming strategy identifies the item or collection processed –Creator indicates the Menu Label of the SEASR Analysis Related Tab links to the items processed in the Analysis No need to install the analysis, it runs as web service

24. MONK Executes flows for each analysis requested –Predictive modeling using Naïve Bayes –Predictive modeling using Support Vector Machines (SVM) –Feature comparisons

25. SEASR Support in VUE Goal: Provide functionality in VUE to use SEASR flows Implementations: –Add content to map –Get metadata for content –Get information about content

26. Meandre Workbench Web-based UI Components and flows are retrieved from server Additional locations of components and flows can be added to server Create flow using a graphical drag and drop interface Change property values Execute the flow The SEASR project and its Meandre infrastructure are sponsored by The Andrew W. Mellon Foundation

27. Extensible to Analysis that You Create You can leverage the flows we have on your server or request your university to host this analysis You can modify these flows and redeploy You can create new flows –Perhaps you want to see only nouns or verbs –Perhaps you want to see a list of extracted entities You can share these flows back to the community

28. Repository Search & Browse Web Service Interactive Web Application Zotero Upload to Repository Zotero to SEASR : Fedora

29. JSTOR Data for Research:SEASR Accesses APIs Access JSTOR API in SEASR components Use the output of these components with existing SEASR components

30. feedback | login | search central Categories Recently Added Top 50 Submit About RSS Featured Component [read more] Word Counter by Jane Doe Description Amazing component that given text stream, counts all the different words that appear on the text Rights: NCSA/UofI open source license Featured Component [read more] Word Counter by Jane Doe Description Amazing component that given text stream, counts all the different words that appear on the text Rights: NCSA/UofI open source license Featured Flow [read more] FPGrowth by Joe Does Browse By Joe Doe Rights: NCSA/UofI Description: Webservices given a Zotero entry tries to retrieve the content and measure its By Joe Doe Rights: NCSA/UofI Description: Webservices given a Zotero entry tries to retrieve the content and measure its Type Component Flows Categories Image JSTOR Zotero Name Author Centrality Readability Upload Fedora SEASR Central Sharing and finding flows and components

31. Discussion Questions What kinds of data assets are you interested? What analysis would you like to use against this data?