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University of Illinois
Tools for Unstructured TextNICAR 2012
Loretta Auvil, UIUCChase Davis, CIR
February 2012
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University of Illinois
Hottest Analytics
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University of Illinois
Overview
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University of Illinois
Sites that List Tools
• DiRT, Digital Research Tools• http://digitalresearchtools.pbworks.com
• Kdnuggets• http://www.kdnuggets.com/software/text.html
• text-processing.com
• Discussion Groups• Text Analytics on linkedin
• http://www.linkedin.com/groups/Text-Analytics-115439• Visual Analytics on linkedin
• http://www.linkedin.com/groups/Visual-Analytics-80552
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University of Illinois
Bad News
• There is no open and freely available tool that is going to solve all your problems!!!
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University of Illinois
Good News
• There is a variety of tools that can be beneficial and must be used in combination to accomplish the goal!!!
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University of Illinois
Natural Language Processing (NLP)• Tokenization
• Part of speech tagging
• Stemming
• Stop word removal
• Other transformations
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University of Illinois
NLP Tools• NLTK
• http://www.nltk.org/
• http://text-processing.com
• OpenNLP• http://incubator.apache.org/opennlp/
• Stanford CoreNLP• http://nlp.stanford.edu/software/corenlp.shtml
• Mallet• http://mallet.cs.umass.edu/
• GATE
• http://gate.ac.uk/
• LingPipe
• http://alias-i.com/lingpipe/
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University of Illinois
Entity Extraction
• Finding entities, like People, Locations, Time, etc
• Some have ability to add your own entities (with seed terms)
• Tools
• OpenNLP
• Stanford CoreNLP
• OpenCalais
• GATE
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University of Illinois
Journalism Application
• Structuring unstructured data
• Social networks of entities
• Clustering
• Plotting data on time line
• Plotting locations on a map
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University of Illinois
Information Extraction
• Automatically identifies and extracts binary relationships from English sentences
• TextRunner
• NACTEM, MEDIE• http://www.nactem.ac.uk/medie/
• ReVerb
• http://reverb.cs.washington.edu/
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University of Illinois
Information Extraction: ReVerb
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University of Illinois
Question and Answer
• Parse the question to determine what type of information needs to be returned
• Leveraging approaches like the information extraction for retrieving the results
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University of Illinois
Journalism Application
• Engagement! Help users find facts relevant to their situations.
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University of Illinois
Document Classification
• Starts with a training set
• Predicts what class a document belongs
• Leveraging pure data mining approaches like Naïve Bayes, Decision Trees, Neural Networks
• Tools• NLTK• Mallet• Weka• Rapid Miner• GATE• Meandre
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University of Illinois
Journalism Application
• IBM's ManyBills project• Identifies the topic of each section in a Congressional bill for
the purposes of identifying outliers. • For example, if a Congressman proposes a bill about the
environment, but it has a section deep down about banking regulation, ManyBills would identify that as an outlier and highlight it.
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University of Illinois
Document Similarity/Clustering
• TF-IDF (Term frequency * inverse document frequency)
• Overview project (AP)• Tools
• GATE• Rapid Miner
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University of Illinois
Journalism Application
• Identifying copycat legislation from year to year
• Clustering documents to find trends
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University of Illinois
Topic Modeling
• Exploratory approach to find patterns by finding words that frequently occur together
• Document can have multiple topics• Words can exist in multiple topics• Tools
• Mallet uses LDA (latent Dirichlet allocation)• Other implementations as well…
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University of Illinois
Topic Exploration
• Topical Guide: • http://tg.byu.edu/
• Tmve (Topic Model Visualization Engine) • http://code.google.com/p/tmve/
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University of Illinois
Topical Guide
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University of Illinois
Tmve
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University of Illinois
Journalism Application
• Reporting tool for making sense of corpus• Isolating topics allows the user to focus only on the documents
in a corpus that are relevant. • There exists a clear potential for more data visualization.
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University of Illinois
Automatic Summarization• Identifies sentences from among the documents
• Identifies common information conveyed across all the documents and then reformulates new sentences expressing that information
• Aims to combine the main themes with completeness, readability, and conciseness
• Lots of algorithms, but not really software tools to download to run on your collection
• Meandre implements a HITS algorithm that identifies sentences but does not reformat them
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University of Illinois
Journalism Application
• Newsblaster• Summarizing all the news on the web• Every night, the system crawls a series of Web sites, downloads
articles, groups them together into "clusters" about the same topic, and summarizes each cluster.
• http://newsblaster.cs.columbia.edu/
• Ultimate Research Assistant• http://ultimate-research-assistant.com
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University of Illinois
Sentiment Analysis
• NLTK• http://www.text-processing.com/demo/sentiment
• APIs• AlchemyAPI• Open Dover• Lexalytics• Saplo
• Meandre (concept tracking)
• Sentiment Analysis Symposium• http://sentimentsymposium.com/agenda.html• May 8, 2012 in New York
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University of Illinois
Journalism Application
• Tracking Twitter sentiment about political candidates
• Comparing the tone of political statements over time or between candidates
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University of Illinois
Analysis Frameworks
• Meandre• http://seasr.org/meandre
• DocumentCloud• www.documentcloud.org/
• Rapid Miner
• http://rapid-i.com/
• Weka• http://www.cs.waikato.ac.nz/ml/weka/index.html
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University of Illinois
Locations
Components
Flows
• 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
Meandre Workbench
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University of Illinois
Meandre Services from Firefox Plugin
Tag Cloud Analysis
Readability Analysis
Automatic Summarization
Network Analysis
Location Entity to Google Map
Date Entity to Simile Timeline
Example: Zotero, SEASR, Protovis, Google Maps,
Simile
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University of Illinois
Topic Modeling
Uses Mallet Topic Modeling to cluster nouns from almost 4000 documents from 19th century with 10 segments per document
Example below is clustering the Bible and shows 8 topics with at most 200 keywords for that topic
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University of Illinois
Concept Mapping
Sentiment Analysissix core emotions (Love, Joy, Surprise, Anger, Sadness, Fear)
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University of Illinois
Correlation Analysis
• Corrected OCR errs with spellchecking