Language Intelligence

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

Robert Munro

With thanks!

Gary King & Jana Thompson:

<- other Idibon people here:Michelle Casbon & Nick Gaylord

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

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

The most honest sentiment analysis results you will see

Accuracy

F-Score Recall Precision F-Score

PositiveNegativ

e NeutralPositiv

eNegativ

e NeutralPositiv

eNegati

ve NeutralSemantria 0.59 0.59 0.56 0.47 0.78 0.68 0.80 0.45 0.62 0.59 0.57MonkeyLearn 0.50 0.38* 0.84 0.54 0.00 0.45 0.60 0.00 0.59 0.57 0.00MetaMind 0.66 0.66 0.68 0.46 0.88 0.78 0.88 0.50 0.73 0.60 0.64Idibon 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

Data beats algorithms; feedback beats data

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.457 0.473

0.615

0.948precisionrecallF-value

Distinguishing the correct ‘Ford’

Distinguishing “Ford” the company from people called “Ford”

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

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

The Solution

• A different economic models for useful sentiment analysis: • Data-sharing for more

accurate training data • Protecting sensitive data

from public release

Machine learning

Optimization

Human annotation

Cloudprediction

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

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

Idibon Public: our implementation

• Free product, offered in addition to our enterprise Idibon Studio and Idibon Terminal solutions

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

Language Intelligence

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

QUESTIONS?Robert Munro