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Your Grandmother Doesn’t Like Surprises A case study of ANM’s Travel Site

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

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  1. Your Grandmother Doesn’t LikeSurprisesA case study of ANM’s Travel Site Jeffrey Catlin – Lexalytics, Inc. Bob Pierce – Fast Search & Transfer May 3, 2005

  2. 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

  3. 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

  4. 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

  5. Site Features

  6. Site Features – 4 star:NYC (the best)

  7. Site Features-4 Star:NYC (the worst)

  8. 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

  9. 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

  10. Knowledge Management in Action Let’s drill in to see how reviews are scored

  11. Knowledge Management in Action Let’s score this review

  12. 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

  13. 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.

  14. 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

  15. 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”

  16. 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

  17. 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

  18. 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

  19. 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

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