Technology Frontiers: Text,
Sentiment, and Sense
Seth Grimes
@sethgrimes
A Sensemaking Story
New York Times,
September 30, 2012
New York Times,
September 8, 1957
Valium: A Chain of Connections
Natural Language Processing
By H.P. Luhn, in
IBM Journal,
April, 1958
http://altaplana.com/ibm-
luhn58-LiteratureAbstracts...
Modelling Text
“Statistical information derived from word frequency and distribution is
used by the machine to compute a r...
New York Times,
September 8, 1957
Luhn’s Example
Close Reading
Can Software Make the Connection?
Mark Lombardi, George W. Bush, Harken Energy
and Jackson Stephens, c. 1979-90, Detail
Insight from Connections
… via graphs, clusters, categories, and counts.
… by mining the full set of available data.
http://techpresident.com/news/21618/pol
itico-facebook-sentiment-analysis-bogus
Online & Social Change Everything
(Accessible) Data Everywhere
Lexical, syntactic, and semantic analysis discern
features including relationships in source materials.
Features = entitie...
How?
From POS to Relationships
Understand parts of
speech (POS), e.g. –
<subject> <verb>
<object> –to
discern facts and
relatio...
Clustered Clarity
Carrot2.
(open source)
Platforms and ecosystems.
APIs and services.
Text and content analytics --
Discerns and extracts features including relati...
Content, Composites, Connections
Content, Composites, Connections, 2
Social Sources
Sentiment Analysis
“Sentiment analysis is the task of identifying positive
and negative opinions, emotions, and evaluation...
Detection, Classification
Beyond Polarity
Intent Analysis
http://www.aiaioo.com/whitepapers/intention_analysis_use_cases.pdf
http://sentibet.com/
Complications
Sentiment may be of interest at multiple levels.
Corpus / data space, i.e., across multiple sources.
Documen...
Audio including speech.
Images.
Video.
http://www.geekosystem.com/
facebook-face-recognition/
http://www.sciencedirect.com...
Sensemaking
“It is convenient to divide the entire
information access process into two
main components: information retrie...
Apply new tech to old needs, e.g., automated coding.
Select from and use all available data.
Marry social to profiles and ...
Racing On
Technology Frontiers: Text,
Sentiment, and Sense
Seth Grimes
@sethgrimes
Technology Frontiers: Text, Sentiment, and Sense by Seth Grimes of Alta Plana Corporation - Presented at the Insight Innov...
Upcoming SlideShare
Loading in...5
×

Technology Frontiers: Text, Sentiment, and Sense by Seth Grimes of Alta Plana Corporation - Presented at the Insight Innovation eXchange North America 2013

391

Published on

A basic definition: Text analytics transforms text-sourced information into data to help you generate insights that fuel better-informed business decision-making. Methods are applied to online and social information, as well as enterprise feedback, to complement and extend traditional and emerging research methods. Text analytics is the leading opinion mining technique, evolving to link emotion and intent signals to behaviors, profiles, and transactions. If text analytics isn’t part of your data toolkit, it should be; if you’re already exploiting text analytics, you’ll want to stay on top of developments. Seth Grimes, in this What’s Next talk, will tell you how.

Published in: Business, Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
391
On Slideshare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
13
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Technology Frontiers: Text, Sentiment, and Sense by Seth Grimes of Alta Plana Corporation - Presented at the Insight Innovation eXchange North America 2013

  1. 1. Technology Frontiers: Text, Sentiment, and Sense Seth Grimes @sethgrimes
  2. 2. A Sensemaking Story New York Times, September 30, 2012
  3. 3. New York Times, September 8, 1957 Valium: A Chain of Connections
  4. 4. Natural Language Processing By H.P. Luhn, in IBM Journal, April, 1958 http://altaplana.com/ibm- luhn58-LiteratureAbstracts.pdf
  5. 5. Modelling Text “Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the auto-abstract.” -- H.P. Luhn, The Automatic Creation of Literature Abstracts, IBM Journal, 1958. Luhn’s analysis of Messengers of the Nervous System, a Scientific American article http://wordle.net, applied to the NY Times article
  6. 6. New York Times, September 8, 1957 Luhn’s Example
  7. 7. Close Reading
  8. 8. Can Software Make the Connection? Mark Lombardi, George W. Bush, Harken Energy and Jackson Stephens, c. 1979-90, Detail
  9. 9. Insight from Connections … via graphs, clusters, categories, and counts. … by mining the full set of available data.
  10. 10. http://techpresident.com/news/21618/pol itico-facebook-sentiment-analysis-bogus Online & Social Change Everything
  11. 11. (Accessible) Data Everywhere
  12. 12. Lexical, syntactic, and semantic analysis discern features including relationships in source materials. Features = entities, measure-value pairs, concepts, topics, events, sentiment, and more. Text analytics may draw on: • Lexicons & taxonomies. • Statistics. • Patterns. • Linguistics. • Machine learning. Text Analytics
  13. 13. How?
  14. 14. From POS to Relationships Understand parts of speech (POS), e.g. – <subject> <verb> <object> –to discern facts and relationships. Semantic networks such as WordNet are a disambiguation asset.
  15. 15. Clustered Clarity Carrot2. (open source)
  16. 16. Platforms and ecosystems. APIs and services. Text and content analytics -- Discerns and extracts features including relationships from source materials. Features = entities, key-value pairs, concepts, topics, events, sentiment, etc. Provide (for) BI on content-sourced data. Data integration, record linkage, data fusion. The Back End
  17. 17. Content, Composites, Connections
  18. 18. Content, Composites, Connections, 2
  19. 19. Social Sources
  20. 20. Sentiment Analysis “Sentiment analysis is the task of identifying positive and negative opinions, emotions, and evaluations.” -- Wilson, Wiebe & Hoffman, 2005, “Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis” “Sentiment analysis or opinion mining is the computational study of opinions, sentiments and emotions expressed in text… An opinion on a feature f is a positive or negative view, attitude, emotion or appraisal on f from an opinion holder.” -- Bing Liu, 2010, “Sentiment Analysis and Subjectivity,” in Handbook of Natural Language Processing
  21. 21. Detection, Classification
  22. 22. Beyond Polarity
  23. 23. Intent Analysis http://www.aiaioo.com/whitepapers/intention_analysis_use_cases.pdf http://sentibet.com/
  24. 24. Complications Sentiment may be of interest at multiple levels. Corpus / data space, i.e., across multiple sources. Document. Statement / sentence. Entity / topic / concept. Human language is noisy and chaotic! Jargon, slang, irony, ambiguity, anaphora, polysemy, synonymy, etc. Context is key. Discourse analysis comes into play. Must distinguish the sentiment holder from the object: “Geithner said the recession may worsen.”
  25. 25. Audio including speech. Images. Video. http://www.geekosystem.com/ facebook-face-recognition/ http://www.sciencedirect.com/science /article/pii/S0167639312000118 http://flylib.com/books/en/2.495.1.54/1/ Beyond Text
  26. 26. Sensemaking “It is convenient to divide the entire information access process into two main components: information retrieval through searching and browsing, and analysis and synthesis of results. This broader process is often referred to in the literature as sensemaking. Sensemaking refers to an iterative process of formulating a conceptual representation from of a large volume of information. Search plays only one part in this process.” -- Marti Hearst, 2009 http://searchuserinterfaces.com/
  27. 27. Apply new tech to old needs, e.g., automated coding. Select from and use all available data. Marry social to profiles and surveys. Factor in behaviors. Interpret according to context and needs. Understand intent to create situational predictive models. Explore; experiment. Suggestions
  28. 28. Racing On
  29. 29. Technology Frontiers: Text, Sentiment, and Sense Seth Grimes @sethgrimes
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×