More Text Analytics

National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-Champaign

Outline

• Concept Tracking

– Emotion Tracking

• Topic Modeling

• Hands-On

Text Analytics: Concept Tracking• Given: Set of documents

• Given: Set of concepts and related words

• Find the concepts in the set of documents using the related words and a synonym network

• Concepts can then be displayed with additional meta data from the documents for timeline, or GIS mapping

• Specific example is Emotion Tracking

SEASR @ Work – Emotion TrackingGoal is to have this type of Visualization to track

emotions across a text document (Leveraging flare.prefuse.org)

Text Analytics: Emotion Tracking• Sentiment Analysis

Sentiment Analysis

• Classifying text based on its sentiment– Determining the attitude of a speaker or a

writer– Determining whether a review is

positive/negative

• Ask: What emotion is being conveyed within a body of text?– Look at only adjectives

• lots of issues and challenges

• Need to Answer:– What emotions to track?– How to measure/classify an adjective to one of

the selected emotions?– How to visualize the results?

Sentiment Analysis: Emotion Selection• Which emotions:

– http://en.wikipedia.org/wiki/List_of_emotions

– http://changingminds.org/explanations/emotions/basic%20emotions.htm

– http://www.emotionalcompetency.com/recognizing.htm

• Parrot’s classification (2001)

– six core emotions

– Love, Joy, Surprise, Anger, Sadness, Fear

Sentiment Analysis: Emotions

Sentiment Analysis: Using Adjectives• How to classify adjectives:

–Lots of metrics we could use …

• Lists of adjectives already classified

– http://www.derose.net/steve/resources/emotionwords/ewords.html

– Need a “nearness” metric for missing adjectives

–How about the thesaurus game ?

Ontological Association (WordNet)

• As of 2006, the database contains about 150,000 words organized in over 115,000 synsets for a total of 207,000 word-sense pairs

POS Unique Strings

Synsets Total Strings Word-Sense Pairs

Noun 117798 82115 146312

Verb 11529 13767 25047

Adjective 21479 18156 30002

Adverb 4481 3621 5580

Totals 155287 117659 206941

Ontological Association (WordNet)

Search for table• Noun

– S: (n) table, tabular array (a set of data arranged in rows and columns) "see table 1”

– S: (n) table (a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs) "it was a sturdy table”

– S: (n) table (a piece of furniture with tableware for a meal laid out on it) "I reserved a table at my favorite restaurant”

– S: (n) mesa, table (flat tableland with steep edges) "the tribe was relatively safe on the mesa but they had to descend into the valley for water”

– S: (n) table (a company of people assembled at a table for a meal or game) "he entertained the whole table with his witty remarks”

– S: (n) board, table (food or meals in general) "she sets a fine table"; "room and board”

• Verb

– S: (v) postpone, prorogue, hold over, put over, table, shelve, set back, defer, remit, put off (hold back to a later time) "let's postpone the exam”

– S: (v) table, tabularize, tabularise, tabulate (arrange or enter in tabular form)

Sentiment Analysis

• Using only a thesaurus, find a path between two words

– no antonyms

– no colloquialisms or slang

Sentiment Analysis

• For example, how would you get from delightful to rainy?

SEASR: Sentiment Analysis

• How to get from delightful to rainy ?

['delightful', 'fair', 'balmy', 'moist', 'rainy’]

• sexy to joyless?

['sexy', 'provocative', 'blue', 'joyless’]

• bitter to lovable?

['bitter', 'acerbic', 'tangy', 'sweet', 'lovable’]

SEASR: Sentiment Analysis

• Use this game as a metric for comparing a given adjective to one of the six emotions.

• Assume the longer the path, the “farther away” the two words are.

SEASR: Sentiment Analysis

• Introducing SynNet: a traversable graph of synonyms (adjectives)

Thesaurus Network (SynNet)

• Used thesaurus.com, create link between every term and its synonyms

• Created a large network

• Determine a metric to use to assign the adjectives to one of our selected terms– Is there a path?

– How to evaluate best paths?

SynNet: rainy to pleasant

SynNet Metrics

• Path length

• Number of Paths

• Common nodes

• Symmetric: ab ba

• Unique nodes in all paths

SynNet Metrics: Path Length

• Rainy to Pleasant

– Shortest path length is 4 (blue) Rainy, Moist, Watery, Bland, Pleasant

– Green path has length of 3 but is not reachable via symmetry

– Blue nodes are nodes 2 hops away

SynNet Metrics: Common Nodes• Common Nodes

– depth of common nodes

• Example

– Top shows happy

– Bottom shows delightful

– Common nodes shown in center cluster

SynNet Metrics: Symmetry

Symmetry of path in common nodes

SynNet: Sentiment Analysis

• Step 1: list your sentiments/concepts

– joy, sad, anger, surprise, love, fear

• Step 2: for each concept, list adjectives

– joy: joyful, happy, hopeful

– surprise: surprising ,amazing, wonderful, unbelievable

• Step 3: for each adjective in the text, calculate all the paths to each adjective in step 2

• Step 4: pick the best adjective (using metrics)

SynNet: Sentiment Analysis

• Example:

– the adjective incredible is more like which emotion

SynNet: Sentiment Analysis

• Incredible to loving (concept: love)

Blue paths are symmetric paths

SynNet: Sentiment Analysis

• Incredible to surprising (concept: surprise)

Blue paths are symmetric paths

SynNet: Sentiment Analysis

• Incredible to joyful (concept: joy)

SynNet: Sentiment Analysis

• Incredible to joyless (concept: sad)

SynNet: Sentiment Analysis

• Incredible to fearful (concept: fear)

SynNet: Sentiment Analysis

• Incredible to wonderful (concept: joy)

SynNet: Sentiment Analysis

• Try it yourself:

• http://services.seasr.org/synnet

– <host>/synnet/path/white/afraid

– <host>/synnet/path/white/afraid?format=xml

– <host>/synnet/path/white/afraid?format=json

– <host>/synnet/path/white/afraid?format=flash

– Database is only adjectives

– More api coming soon, visualizations

Sentiment Analysis: Issues

• Not a perfect solution– still need context to get quality

• Vain

– ['vain', 'insignificant', 'contemptible', 'hateful']

– ['vain', 'misleading', 'puzzling', 'surprising’]

• Animal

– ['animal', 'sensual', 'pleasing', 'joyful']

– ['animal', 'bestial', 'vile', 'hateful']

– ['animal', 'gross', 'shocking', 'fearful']

– ['animal', 'gross', 'grievous', 'sorrowful']

• Negation

– “My mother was not a hateful person.”

Sentiment Analysis: Process

• Process Overview (2 flows)– Create Concept Cache & Ignore Cache

• Load the documents

• Extract the adjectives (POS analysis)

• Find the unique adjectives

• Label each adjective (SynNet Service)

– Apply Concepts • Load the document(s)

• Segment the document for single document

• Extract the adjectives (POS analysis)

• Summarize adjectives across segments or documents

• Visualize the concepts by segments

Sentiment Analysis: Visualization

• SEASR visualization component– Based on flash using the flare ActionScript Library

• http://flare.prefuse.org/

– http://demo.seasr.org:1714/public/resources/data/viewer/emotions.html

Sentiment Analysis: 911 Corpus• Concepts for each story were identified as

before

• Mapping was done by using additional meta-data for each story

Concept Mapping of an Author

• 5 books

• Charles Dickens

1. Tale of Two Cities

2. Great Expectations

3. Christmas Carol

4. Oliver Twist

5. David Copperfield

Concept Mapping for Multi Documents

Concept Mapping of a Single Document

Tale of Two CitiesGreat Expectations

Concept Mapping of a Single Document

Concept Mapping: Creating Cache Files

• Two cache files

– Concept cache

• Stores the word, concept, POS, seed word mapping and some numbers

– great joy JJ 0 31 wonderful2

– anonymous surprise JJ 3 561unbelievable 4

– dark fear JJ 8 1502 horrible2

– Ignore cache

• Stores the word that do not map to a concept

Concept Mapping: Create Cache Flow

Concept Mapping Notes

• If list of concepts and seed words have not changed, you can continue to use the same cache files for all documents. But you will need to change the cache file it you want to define new concept mappings.

– E.g. Emotion Tracking: 6 concepts and their seed words

– E.g. Positive/Negative: 2 concepts and seeds like (yes, yeah, ok, etc) (no, nay, not, etc)

– E.g. Male/Female: 2 concepts and seeds like (he, his, him, mr, etc.) (she, her, mrs, etc.)

• Copy cache files to your machine for starters

Topic Modeling

• Uses Mallet Topic Modeling to cluster nouns from over 4000 documents from 19th century with 10 segments per document

• Top 10 topics showing at most 200 keywords for that topic

Topic Modeling Process

• Load the documents

• Segment the documents

• Extract nouns (POS analysis)

• Create the Mallet data structures for each segment

• Mallet for topic modeling

• Save results

• Parse keyword results

• Create tagclouds of keywords

Demonstration

• Entity Extraction for timelines, maps, and social networks

• Concept Tracking

– Emotion Tracking for single document

– Emotion Tracking comparison for multiple documents

• Topic Modeling

– Tagclouds of topic keywords

Learning Exercises

• Construct flow for performing entity extraction and review results.

– Determine what you want to do with these results.

• Open the flow for tracking concepts

– Modify the flow to load your data

– Modify the flow to track concepts of interest to you

Attendee Project Plan

• Study/Project Title

• Team Members and their Affiliation

• Procedural Outline of Study/Project– Research Question/Purpose of Study

– Data Sources

– Analysis Tools

• Activity Timeline or Milestones

• Report or Project Outcome(s)

• Ideas on what your team needs from SEASR staff to help you achieve your goal.

Identify Analytics

Discussion Questions

• What part of these applications can be useful to your research?