Text analytics software and business processes apply natural language processing to extract business insights from text sources like social media, online content, and enterprise data. The document discusses what is currently working well in text analytics, such as its application in conversation, customer experience, finance, healthcare, and media, as well as its use of techniques like bag-of-words modeling and entity extraction. The document also outlines emerging areas for text analytics, such as analysis of narrative, argumentation, integration of multiple data sources and languages, and understanding of affect and emotion.

1. Text Analytics Market
Insights: What's Working &
What's Next?
Seth Grimes
Alta Plana Corporation
@sethgrimes
November 8, 2017

4. Level setting:
Text analytics is a term for software and business
processes that apply natural language processing to
extract business insights from social, online, and
enterprise text sources.
1.Software.
2.Business processes.
3.Business insights.
4.Data sources.
Note: Commercial product images on the slides that follow
are used for illustration only.

5. Search BI
Text
AnalyticsSemantic search
Information access /
question answering
Integrated analytics
Data
Mining
Text data mining
“Text analytics has been part of the BI, data science,
and analytics toolkit for over a dozen years.”
Data science?

6. Clustering and classification ⇒ extraction
https://www.slideshare.net/ogrisel/statistical-machine-learning-for-
text-classification-with-scikitlearn-and-nltk

7. Insights: Identity (entities, topics, aspects, …)

8. Insights: Sentiment

9. Insights: Personality

10. Insights: Intent

11. “Lexis Answers… is a new time saving feature that utilizes
artificial intelligence (A.I.). Integrating powerful machine
learning, cognitive computing and advanced natural
language processing technologies, Lexis Answers transforms
legal research by understanding the user’s natural language
question and delivering the clearest, most concise and
authoritative answer, in addition to finely tuned,
comprehensive search results.

12. “’Dynamic program generation,’ allows Viv to
understand the intent of the user and to create
programs to handle tasks on the fly.”
Insights: Intent

13. Emerging Conversational AI

16. Tech category errors:
https://twitter.com/PetiotEric/status/916152310954385409

18. What’s Working
Application spaces: conversation, CX, finance, healthcare &
life sciences, media, politics & policy.
Bag of Words; stats; rules; taxonomy; lexical networks.
Thing finding + attributes: topic extraction; entity resolution;
medium-grained sentiment.
Word sequences (n-grams, skip-grams, lexical nets).
Single-source/-type data.
More-resourced languages.

19. What’s Next
Usable narrative / conversation / argumentation analysis.
Generation of persuasive narrative / argumentation.
Cross-source/-type validation and data integration.
Lesser-resourced languages.
Affect (emotion) understanding + generation.
Human-understandable NLP models.
… sequences, machine learning, language generation.

20. https://aws.amazon.com/blogs/ai/train-
neural-machine-translation-models-with-
sockeye/
“In natural language processing (NLP), many tasks
revolve around solving sequence prediction
problems. For example, in MT, the task is
predicting a sequence of translated words, given
a sequence of input words. Models that perform
this kind of task are often called sequence-to-
sequence models.”

21. Computational Narrative Intelligence:
Past, Present, & Future
Mark Riedl
riedl@cc.gatech.edu
@mark_riedl

22. Conversation / interaction

23. https://www.slideshare.net/myassignmenthelpnet/natural-language-processing-help-at-
myassignmenthelpnet

25. https://arxiv.org/pdf/1412.5567v2.pdf, December 2014

26. Reinforcement Learning for NLP
Advanced Machine Learning for NLP
Jordan Boyd-Graber
REINFORCEMENT OVERVIEW, POLICY GRADIENT
Adapted from slides by David Silver, Pieter Abbeel, and John Schulman
Advanced Machine Learning for NLP | Boyd-Graber Reinforcement Learning for NLP | 1 of 20

27. Platforms and tools?

32. Market / opportunity space?
• What do you need? A tool, solution, or service?
• What’s your platform preference?
• What’s your market?
• What’s mode or packaging do you require?
• …
A few examples of operationalization…

36. Differentiation
Data assets.
Domain/task adaptation.
Platform or delivery model.
Usefulness.
Usability. Friction.

37. Text Analytics Market
Insights: What's Working &
What's Next?
Seth Grimes
Alta Plana Corporation
@sethgrimes
November 8, 2017