a new approach to real time intent and sentiment analysis

A New Approach to Real Time Intent and Sentiment Analysis

Aloke Guha Kapil Tundwal

Sentiment Analysis Symposium March 5-6, 2014, New York

Cruxly

Sentiment?

Why business cares about intents?

Find Customers Build Sell Support

Why intent is hard to detect?

Grammar

Punctuation Spelling Sarcasm

How

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

Source-Agnostic

• SMS, Emails, Social Media • Mobile apps, Location-based services

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

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

Examples

Intent Summary

Intent Summary: Comparison Across Brands

Inte

nt: B

uy

Leads

Inte

nt: L

ike

Inte

nt: D

islik

e

Inte

nt: Q

uest

ions

/Req

uest

s

Geo-Location

Intent for Iterative Analysis

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

Conclusions

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

Thank You

info@cruxly.com facebook.com/cruxly www.cruxly.com

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