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Why Sentiment Analysis is a Market for Lemons … and How to Fix it

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New technical and economic models for improving sentiment analysis and other machine learning services.
Presented at "Data Day Texas", January 2016.

Veröffentlicht in: Technologie
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Why Sentiment Analysis is a Market for Lemons … and How to Fix it

  1. 1. Language Intelligence Why Sentiment Analysis is a Market for Lemons … and How to Fix it Robert Munro
  2. 2. With thanks! Gary King & Jana Thompson: <- other Idibon people here: Michelle Casbon & Nick Gaylord
  3. 3. What is a market for lemons? • Information asymmetry between buyers and sellers, leaving only "lemons" behind. George Akerlof • Buyers cannot distinguish good from bad products • Prices are equally low for all products • The buyer's price adverse selection problem drives the high-quality products from the market
  4. 4. Competition is not increasing accuracy • 100+ companies offering some form of sentiment analysis • Accuracy hovering around 70% for real-world applications for almost a decade
  5. 5. The most honest sentiment analysis results you will see Accuracy F-Score Recall Precision F-Score Positive Negative Neutral Positive Negative Neutral Positive Negative Neutral Semantria 0.59 0.59 0.56 0.47 0.78 0.68 0.80 0.45 0.62 0.59 0.57 MonkeyLearn 0.50 0.38* 0.84 0.54 0.00 0.45 0.60 0.00 0.59 0.57 0.00 MetaMind 0.66 0.66 0.68 0.46 0.88 0.78 0.88 0.50 0.73 0.60 0.64 Idibon Public 0.68 0.67 0.76 0.75 0.49 0.66 0.69 0.72 0.71 0.72 0.58 • Even within the best results for one domain, there is no clear leader when broken down by category • All systems could have best results in other domains • All could adapt here: Monkey Learn had errors with the ‘Neutral’ category, but we are sure they could update their models Source: Sentiment 140 corpus, 3-way sentiment on social data: http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip
  6. 6. Data beats algorithms; feedback beats data 0.457 0.473 0.615 0.948 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Linear model Deep Learning In-domain training 10mins analyst feedback precision recall F-value Distinguishing the correct ‘Ford’ Distinguishing “Ford” the company from people called “Ford”
  7. 7. Consumers are uncertain • When consumers try out- of-domain analysis, they lose confidence from the poor results. • Domain-dependence means that even bad models will be accurate in some areas • Consumers can only evaluate anecdotally or by precision, not recall • Uncertainty prevails
  8. 8. Market forces are not breeding innovation • Can’t innovate through code alone • More training data! • But low price-points means low margins • Lack of capital to find & label enough training data
  9. 9. The Solution • A different economic models for useful sentiment analysis: • Data-sharing for more accurate training data • Protecting sensitive data from public release
  10. 10. Machine learning Optimization Human annotation Cloud prediction engine Actionable intelligence On-site prediction engine Copy & Sync Models App Requests Ambiguous, Novel & Interesting Items Internal Data Flow Hybrid Model Data Flow Application Data Flow firewall
  11. 11. The Benefits • Multiple organizations can share in the benefits of better sentiment analysis, without sacrificing privacy • Single point of human-contact: no expensive duplicate manual labeling of data • Keeps lemons out of the market
  12. 12. Idibon Public: our implementation • Free product, offered in addition to our enterprise Idibon Studio and Idibon Terminal solutions
  13. 13. Applies to NLP and Machine Learning more broadly Every human communication • Any task can be bundled this way • Allows margins for use cases that were not otherwise viable • … including the full diversity of languages, priced out when everyone started in English
  14. 14. Language Intelligence Why Sentiment Analysis is a Market for Lemons … and How to Fix it QUESTIONS? Robert Munro