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Understanding and Evaluating Big Data Text Analytics Solutions
 

Understanding and Evaluating Big Data Text Analytics Solutions

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As social media is transforming enterprises and consumer services, unstructured data is growing more important to business and business architecture. In this presentation I outline ...

As social media is transforming enterprises and consumer services, unstructured data is growing more important to business and business architecture. In this presentation I outline
- advancements and challenges in social text analytics and its role in big data analytics in the years to come.

- mistakes companies have made by applying text analytics without understanding its power or limitations.

- how machine learning is opening up for new applications of text analytics and how to evaluate the performance of text analytics systems.

- a few use cases for social text analytics and explore it's potential for extracting new insights from existing data sources.

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  • The green cells here are where the two coders agree. We can use this to derive a “raw” accuracy score. We add up the total number of instances where the two coders agree (the green cells) and divide by the total number of instances (1500) – to get a raw accuracy score of 61.5%.This raw accuracy score provides the first benchmark against which we can assess machine performance. Put concretely, if we can get a machine to classify documents for sentiment where a human would agree with its classifications around 60% percent of the time, our machine would be performing as well as a human being.
  • Remember, we said before that not all mistakes are made equally. It depends on the use to which you’re putting the data. In most situations, however, it’s worse to mislabel something positive as negative than it is to mislabel something positive as neutral. This is true both for a human or machine coder.We can factor in these relative weights by using what is called a Credit Matrix. This says that you get 100% when your label agrees with the gold standardUltimately, the PCFM will establish the baseline against which we measure the performance of our machine learning algorithm.

Understanding and Evaluating Big Data Text Analytics Solutions Understanding and Evaluating Big Data Text Analytics Solutions Presentation Transcript

  • Presented by Vidar Brekke, Social Intent LLCSOCIAL TEXTANALYTICS FORENTERPRISE ANDCONSUMERAPPLICATIONSThe International Association of SoftwareArchitects. October 23, 2012 @ividar #nlproc
  • What is Text Analytics? Processes that uncover business value in A unstructured text via the application of statistical, B linguistic, machine C learning, and data analysis and visualization techniques @ividar #nlproc 2
  • Text analytics help answerbusiness questions faster andcheaper than before, uncoveringnew, hidden insights! @ividar #nlproc 3
  • Text analytics is a Big Data problem Volume Velocity Variety Hundreds of languages Social media, help inquiries, email, texts, surveys 10.2 Million tweets sent Cryptic (vertical during the first Formal, inform industry or presidential al or criminal activity) debate ridiculously informal @ividar #nlproc 4
  • I’m So Intextuated With You Unstructured text represents the biggest opportunity and problem in Big Data Text, as opposed to most other enterprise data, it’s very dirty data @ividar #nlproc 5
  • Correlating consumer confidence with mentions of “jobs” onTwitter @ividar #nlproc 6
  • Yay! Steve Jobs launches a new iPhone! @ividar #nlproc 7
  • You can trade on Twitter @ividar #nlproc 8
  • Low Signal/Noise Ratio + Naïve Metrics Lead to Wrong Conclusions • Lack of relevance: Many conversations you think are about you, aren’t. • Poor accuracy: Many automated sentiment solutions are as good as a coin flip. • Generic: All analysis is applied the same way across domains • Language Evolves: Slang, sarcasm is rampant in social media. Dictionary-based approaches are largely ineffective. @ividar #nlproc 9
  • Relevancy: It’s not all about you. Let me finish my drink before you drive me to the Betty Ford clinic! Call me a bigot, but white guys can’t sprint! #london2012 My husband is such a baby. He won’t even taste raw food. Is Delta’s food prepared by Purina? So much for first class. @ividar #nlproc 10
  • Search and Destroy (the data you’re looking for) Text analytics got traction in the 80s, but the use-cases were different than today. “Word spotting” – not different from a Google search. Show me all documents containing: Ford NOT Harrison But it doesn’t scale @ividar #nlproc 11
  • Booleans are like woodcarving with a chainsaw Query: Ford NOT Harrison …. …would miss this tweet Carguy231: Me and a dozen others have lined up outside the Harrison, NY Ford dealership to test drive the new Fusion! @ividar #nlproc 12
  • Booleans are like woodcarving with a chainsaw Query: Ford AND Fusion…. …would get this tweet Roadrunner123: Stuck with my dad in his ford listening to horrible jazz fusion @ividar #nlproc 13
  • Sentiment Analysis Early sentiment analysis tools also use word spotting. “Awesome” = good “Sucks” = bad What about sarcasm, slang, new words? Additionally, the analysis is typically on overall contextual polarity, rather than targeted. “I love the new Camaro, it’s better than the Mustang” @ividar #nlproc 14
  • You can’t use word spotting for sentiment detection “It took all morning to sign the lease papers for my new Mustang!” “I stood on line all morning to get the last Mustang on the lot!” “The brakes on the Mustang are surprisingly unpredictable.” “The TV ads for the Mustang are surprisingly unpredictable!” “The Mustang has never been good” “The Mustang has never been this good” @ividar #nlproc 15
  • Nu-School text analytics is based on Machine Learning Using training-data to help the system to recognize patterns. We develop a statistical probability that a sentence is positive, negative, etc. What are training data? These are samples of text annotated by humans in an effort to show the machine what the right answer is “I love my iPhone, but hate AT&T” | iPhone | Positive | AT&T | Negative Much easier and quicker to develop new languages than dictionary based approaches @ividar #nlproc 16
  • Test: What’s the sentiment here? “Reuters reports that Assad continues the massacre of his own people amid sanctions from the international community.” @ividar #nlproc 17
  • How to evaluate a text analytics platform The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. “I can’t believe the bar has a hidden gambling room in the back!” An automated system can never be better than humans. Or can it? @ividar #nlproc 18
  • Using Human Parallel Coding to Establish Gold Standards Confusion Matrix: Human as Gold Standard POSITIVE NEGATIVE NEUTRAL TOTAL POSITIVE 365 24 159 548 NEGATIVE 57 81 65 203 Raw Accuracy: 61.5% NEUTRAL 274 60 415 749 TOTAL 696 165 639 1500 If human agrees with a machine around 60% percent of the time, the machine would be performing as well as a human being. @ividar #nlproc 19
  • Using A Credit Matrix to Create Improved Measurement POSITIVE NEGATIVE NEUTRAL POSITIVE 100% 0% 50%NEGATIVE 0% 100% 50% Credit Matrix NEUTRAL 50% 50% 100% Partial Credit Figure of Merit: 82.3% POSITIVE NEGATIVE NEUTRAL Confusion Matrix: POSITIVE 365 24 159 Human 1 as Gold NEGATIVE 57 81 65 Standard NEUTRAL 274 60 415 @ividar #nlproc 20
  • Precision & Recall (sentiment as an example) Precision is the fraction of retrieved instances that are relevant E.g. How many instances labeled as positive, were actually positive Recall is the fraction of relevant instances that are retrieved E.g. How many positive instances the system detected compared to all positive instances. @ividar #nlproc 21
  • Top business applications of text/content analytics* *Alta Plana, 2011 • Brand / product / reputation management • Market research and social media monitoring, i.e. what are people saying about my brand or products • Voice of the Customer / Customer Experience Management • Do I need to step in and offer customer service? • How many people recommend my brand vs. advocate against it? • Search, Information Access, or Questions Answering • Which bloggers are negative toward Obamacare? • Which of the hotels on Yelp.com get great reviews for the room service? • What are some articles similar to this one? • Competitive intelligence • What competing products are people considering and why • Are competitor’s media spend generating purchase intent? @ividar #nlproc 22
  • Growing areas for is text analytics being applied Product development Intelligence and counter-terrorism, law enforcement Pharmaceutical drug discovery Financial services and insurance Media, publishing & advertising Political research CRM @ividar #nlproc 23
  • Still awake? There is money in text analytics. Here’s a stock tip worth the price of admission alone (YMMV….) @ividar #nlproc 24
  • Strange Bedfellows Whenever Anne Hathaways name appeared with any regularity in news stories, Berkshire Hathaway A shares rose in value. @ividar #nlproc 25
  • Thx & txt u l8tr Vidar Brekke vidar@socialintent.com @ividar @ividar #nlproc