M12 scenario: Early prototype demo

Miha Grčar (Department of Knowledge Technologies, Jožef Stefan Institute)

& FIRST Consortium

M12 scenario: Early prototype demo

Luxembourg, Nov 2011

Outline

We will show two integrated prototypesTwitter sentiment analysis prototypeSentiment extraction prototype

The aim is to…Give a better idea of the overall FIRST processGive a hint of the final “product” from the

technological perspectiveDemonstrate the collaboration between partners

(integration efforts)

Luxembourg, Nov 2011FIRST Y1 Review Meeting 2

Twitter Sentiment Demo


Architecture, Integration & Scaling Strategy

Man

agem

ent

WP

10

WP2 & WP7

Dis

sem

inat

ion

& E

xplo

itatio

nW

P9

WP3 WP4 WP6

OntologyInfrastructure

InformationExtraction

Sentiment Analysis

Decision SupportInfrastructure

Domain-independent GUI(Open Source)

Information Integration

Data, Information & Knowledge Base

WP5

WP1 & WP8

UC#1Market

Surveillance

UC#2 Reputational

Risk management

UC#3 Online Retail

Brokerage

DataAcquisition

DataAcquisition

FIRST Y1 Review Meeting 3

Sentiment Analysis




Acquisition of

Tweets

SentimentClassificatio

n

ActiveLearning &Visualizatio

n

Database of Tweets

Web-based Infrastructur

e

Basic Concepts

Sentiment classification

Active learning


Sentiment Classification

Labeled examples

Build model (train classifier)

Classify unlabeled examples

Luxembourg, Nov 2011FIRST Y1 Review Meeting

POS Financial markets are now officially open :)POS market intelligence GMI Interactive and Mintel Win ARF Great Minds Award for Quality in ResearchPOS $AAPL : trust me -- AAPL will soar tomorrowNEG Oh how I miss the days with GBP was at least 2 times the AUD. Sterling forecast to hit all-time lows soonNEG omg! did you know BORDERS closed?! they went bankrupt last month and closed!! awww, too bad! i love borders!!NEG @aekins that's just too bad

LabeledExamples

TrainingAlgorithm

Classification

Model

Classification

Algorithm

UnlabeledExamples

Predictions(Labels)

So Nickelodeon filed for bankruptcyand announced that the next Kids Choice

Awards will be it's last.NEG

Classification

Model

5

Active Learning

Labeling examples manually is expensive

Active learning reduces this cost

Experts provide labels only for a (small) subset of examples

Examples in this subset are carefully chosen to produce a classifier that is “as accurate as possible”


Document space (tweets)

Active Learning

7 Luxembourg, Nov 2011FIRST Y1 Review Meeting

Document space (reality)

Active Learning

Negative sentiment

Posi

tive s

enti

ment

Optimal

hyper

plane


Document space (initial guess)

Active Learning


Document space (refinement)

Active Learning


Document space (refinement)

Active Learning


Document space (almost there…)

Active Learning


Acquired Tweets

Active Learning Workflow


Twitter API

Language

Detector

Near-Duplicat

e Remover

Part of Speech Tagger

Training Algorith

m

Classification Model

TweetsLabeledDataset

Twitter API

PreprocessingClassifie

r

Classification Model

Twitter Sentime

nt Results

UserQuery

13

Client

Client Client

Active Learning

Demo video(3:00)

Early Integrated Prototype


Architecture, Integration & Scaling Strategy

Man

agem

ent

WP

10

WP2 & WP7

Dis

sem

inat

ion

& E

xplo

itatio

nW

P9

WP3 WP4 WP6



Sentiment Analysis



Data, Information & Knowledge Base

WP5

WP1 & WP8

UC#1Market

Surveillance

UC#2 Reputational

Risk management

UC#3 Online Retail

Brokerage

DataAcquisition

DataAcquisition

FIRST Y1 Review Meeting 14

Sentiment Analysis




Historical Data

ZeroMQChannel


Visualization



Stream Simulator

Web-basedInfrastructu

re

Early Integrated Prototype


Data Stream Simulator

ZeroMQ Channel

15

Sentiment ExtractorZeroMQ Channel

Historical Data (Documents)

HTTPPushWeb

Server

Client(Browser

)

Client(Browser

)

Client(Browser

)

Documents(XML)

Sentiment Index(Numbers)

Java Java C#

JavaScript

WP3 WP4 WP2/7

WP5

WP2/7

WP6

Demo video(1:00)

Concluding Remarks

Effortless integration of data acquisition, sentiment extraction, and Web-based interface

First “signs of usefulness” for the financial domain

Relationship to UC#2 (reputation) and UC#3 (retail brokerage)
