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Your Grandmother Doesn’t Like Surprises A case study of ANM’s Travel Site. Jeffrey Catlin – Lexalytics, Inc. Bob Pierce – Fast Search & Transfer May 3, 2005. Overview. Project Overview Project Goals Technology Elements Site Features Improved Search Automated Processing of Hotel Reviews

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your grandmother doesn t like surprises a case study of anm s travel site

Your Grandmother Doesn’t LikeSurprisesA case study of ANM’s Travel Site

Jeffrey Catlin – Lexalytics, Inc.

Bob Pierce – Fast Search & Transfer

May 3, 2005

overview
Overview
  • Project Overview
    • Project Goals
    • Technology Elements
  • Site Features
    • Improved Search
    • Automated Processing of Hotel Reviews
  • Knowledge Management in Action
    • Sentiment / Tone capability is unique and fully automated
    • Improvement over 1 to 5 star ratings
  • Customer Reaction and Futures
    • Go Live for this site
    • Other sites utilizing this technology
  • Contacts:
    • Jeff Catlin: jeff@lexalytics.com
    • Bob Pierce: bob.pierce@fastsearch.com
project overview
Project Overview
  • ANM – Associated News Media is a publisher in the UK that is leveraging it’s content to reach into Internet Applications like Travel
  • Project Goals:
    • Improve Stickiness of the site, which is key to generating more add Dollars
    • Improve and simplify the search features of the site, including sorting by a variety of field types and making search available throughout the site
    • Expose and Automate user reviews. Providing accurate and ready access to user reviews improves stickiness and acceptance of the site
    • Reduce the cost of utilizing user reviews
    • Dramatically increase the breadth of coverage of user reviews
project overview1
Project Overview
  • Technology Elements:
    • ANM
      • Custom Application interface
      • Utilizing FAST ESP for search features
    • FAST Marketrac:
      • FAST ESP provides Application Search Features
      • FAST Content Processing Pipeline and web spider for reviews
    • Lexalytics:
      • Salience Server for Scoring hotel and travel reviews
      • Sentiment Toolkit: Build out a travel focused Sentiment/Tone database
knowledge management in action
Knowledge Management in Action
  • Trustworthy User Reviews are a key to the stickiness of the site
  • Reviews are obtained through feeds and spidering:
    • Feeds: IgoUgo & Fodors
    • Spidering: tripadvisor.com & virtualtourist.com
  • Reviews are monitored and updated continuously and processed through the FAST Content Processing Pipeline
  • Automated reviews are more consistent, trusted and up to date than star ratings
    • Unique feature
    • Totally automated and more consistent than human ratings
knowledge management in action1
Knowledge Management in Action
  • How does it all work?
    • Lexalytics provides out-of-the-box sentiment tone analysis
    • Toolkit to build scoring databases for verticals like travel, finance, security
    • System builds up a dictionary of scored phrases that indicate good or bad depending on the vertical it’s used for
      • Phrase scores are determined using a training set and msn search
      • Scores are measuring nearness of phrases with good and/or bad terms
    • Results in a phrase dictionary with phrases like:
      • Sunny Day: 1.2706
      • Unsafe food: -0.7634
    • The Lexalytics Salience Server is embedded within FAST’s Marketrac product, so integration of sentiment/tone is very straightforward
knowledge management in action2
Knowledge Management in Action

Let’s drill in to see how reviews are scored

knowledge management in action3
Knowledge Management in Action

Let’s score this review

knowledge management in action4
Knowledge Management in Action
  • Looking at the scoring of an individual review:
    • Review for Marriott Marquis
    • “Great stay, no elevator problems”
    • Reviews are scored, averaged and displayed on a 1 to 10 scale
customer feedback
Customer Feedback
  • Customer is pleased with the site
    • Goes live today (5/3/05)
    • Tuning of the hotel scoring has allowed the customer to put their own touch on the system, giving them a unique offering
    • Combination of information discovery features and integrated booking should allow ANM to compete with any of the well known travel sites.
information intelligence examples
Information Intelligence Examples
  • Financial news and market analysis
    • Market intelligence portal and alerts for brokers
  • Pharmaceutical competitive analysis
    • Tracking molecules, drugs and companies “in the rear-view mirror”
  • Intellectual property protection
    • Content similarity analysis and alerting
  • Illegal e-commerce
    • Contraband trafficking and the “whack-a-mole” problem
    • Cracking pornography rings
      • Automated image analysis
      • Chat room monitoring and alerting
  • Threat detection and analysis
market intelligence in financial services
Market Intelligence in Financial Services
  • Leading European financial services group
    • Capital markets, insurance, real estate, asset management, securities
    • Goal: Trade more competitively, create better analyst reports
  • Leveraged FAST ESP and FAST Marketrac
    • Collect actionable information ahead of general market availability
      • Premium sources, blogs, local web sites, research reports, etc.
    • Real-time, personalized analysis
      • Search domains selected by individual analysts
      • Correlate price movements with related news
      • Analyze news flow for market-moving potential
    • Communicate and act
      • Minimal latency
      • Profile-based SMS/e-mail alerting
      • Automated “morning reports”
because timing is money first mover advantage in markets

When Reuters published hours later, stock moved 2%. But these traders were already done with the trades....

Speech by CEO at Copenhagen Business School quoted by Danish news site.

Because Timing is Money: First-mover Advantage in Markets
accelerate the decision cycle

Time

Accelerate The Decision Cycle

BETTER Decisions, FASTER!

After

Gather

Discover

Analyze

Decide

ACT

Identify

Search/Gather

Analyze

Decide

ACT

Before

Decision point

Decision point

Impact point

time

futures
Futures
  • Text analysis software has matured to the point where powerful applications can be deployed at a reasonable expense and high degree of confidence
  • Search and Text Analysis will play an increasingly important part in Business Intelligence, High Volume Storage and Consumer Electronics
    • Entity extraction is relatively mature and fairly high-quality
    • Classification (subject and tone) is being deployed in real-world apps
    • Relationships between content elements is on the short-term horizon
slide19

Intellectual Property ProtectionWhen Information, Time are the Assets

  • Article extraction from websites
  • Computation of similarity primitives

Validate content and

determine changes

Seed URL DB

Target Site profile

Real-Time Content Analysis

Detected

matches

WWW

Check similarity

Detailed similarity check

Document

Document

...The Wimbledon

and U.S. Open

...The Wimbledon

champion,

and U.S. Open

seeded second,

Similar doc.

champion,

breezed past...

...The Wimbledon

seeded second,

...The Wimbledon

Similarity vector:

and U.S. Open

breezed past...

Crawler

and U.S. Open

<[wimbledon, 1][US

champion,

champion,

seeded second,

open,

seeded second,

breezed past...

breezed past...

0,7][champion,

0,6]…>

Notification,

Enforcement

Similarity

Similarity

Results

Queries

Sequential analysis compares

longest common subsequenceand maximum overlap.

API - Similarity

Real-time

index

IP Database