Detecting actionable intent in online and messaging text-based posts, especially in near real-time, is becoming significant for customer acquisition, marketing, support and product management. Location based services and wide-spread adoption of mobile devices also increases the importance of intent such as intent to buy or making a commitment, or an occurrence of an event. In this talk and demonstration, we will present a non-traditional approach developed by Cruxly to intent as well as sentiment analysis.

1. A New Approach to Real Time
Intent and Sentiment Analysis
Aloke Guha
Kapil Tundwal
Sentiment Analysis Symposium
March 5-6, 2014, New York
Cruxly

2. Sentiment?

3. Why business cares about intents?
Find
CustomersBuild Sell Support

4. Why intent is hard to detect?
Grammar Punctuation Spelling Sarcasm

5. How
• Grammar-aware parsing
• Verb classification
• Real-time detection
• Horizontal first . . . vertical later

6. Source-Agnostic
• SMS, Emails, Social Media
• Mobile apps, Location-based services

7. Intent Detection Basis*
Event Detection
Text
Extraction
Email / IM
Social
Media
Web Posts
Date
Location
Names
Ext. Opinion
. . .
Event
Detection
Logic
Event
Signals
Tokenization Segmentation
Text
Content
Sentence
Phrase
Text
Units
Parser
Grammar
Rules
Event
Definition
Natural Language Processing
Ref: USPTO 20120245925, “METHODS AND DEVICES FOR ANALYZING TEXT,” Guha, Kireyev, Lampert, and Tundwal, 2012

8. Under the Hood (Twitter case)
Tweets
(Keywords/KIP)
Requests
(Keywords)
Tweet ID + Intent
Signal
(PostgresSQL)
Tweets
Content Store
(DynamoDB)
Cruxly Intent Detection
(AWS)
Reports / Dashboard
Tracker Editor
(web app)
Twitter
Aloke Guha: Analytics Drives Big Data Drives Infrastructure, 29th IEEE MSST 2013
Analytics: Event /
Intent Detection
Source/Device
Metadata: Poster,
Date/Time,
#Followers,
Location, . . .
User Metadata:
Keyword / KIP
Custom: RT / Ad
Hoc Query
Tweets
(Keywords)
Streaming API Client

9. Examples

10. Intent Summary

11. Intent Summary:
Comparison Across Brands

12. Intent:Buy


13. Leads

14. Intent:Like

15. Intent:Dislike


16. Intent:Questions/Requests


17. Geo-Location

18. Intent for Iterative Analysis

19. Future Work
• Better polarity – orthogonal to grammar rules
• ‘Activation’ (accept, agree, etc.) verbs*
• Increase depth analysis
• Different grammars – other languages
*B. Levin, English verb classes and alternations: a preliminary investigation, 1993, University of Chicago Press

20. Conclusions
• Actionable intent and event detection
• Grammar-aware parsing to add semantic basis
• Real-time response with optimized analysis
• Vertical applications

21. Thank You
info@cruxly.com
facebook.com/cruxly
www.cruxly.com

22. Selected References
1. USPTO #20120245925, “Methods and Devices for Analyzing Text,”
Guha, Kireyev, Lampert, and Tundwal, September 27, 2012
2. A. Guha, “Analytics Drives Big Data Drives Infrastructure,” Keynote
presentation, 29th IEEE Mass Storage Conference, May 2013.
3. B. Levin, English verb classes and alternations: a preliminary
investigation, 1993, University of Chicago Press.
4. A. Esuli, S. Baccianelli, and F. Sebastiani, “SentiWordNet 3.0: An
enhanced lexical resource for sentiment analysis and opinion mining,”
Proc. 7th Conf Intl.’ Language Resources Evaluation, May 2010.
5. E. Cambria, C. Havasi and A. Husain, “SenticNet 2: A semantic and
effective resource for opinion mining and sentiment analysis,” Proc.
FLAIRS Conf., 2012
6. A. Gangemi, et al, “Frame-Based Detection of Opinion Holders and
Topics: A Model and a Tool,” IEEE Computational Intelligence, Feb.
2014