Deborah A. Dahl Conversational Technologies Chair, W3C Multimodal - - PowerPoint PPT Presentation

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Natural Language Processing for Sentiment Analysis Using the MMI Architecture Get Smart: Smart Homes, Cars, Devices and the Web W3C Workshop on Rich Multimodal Application Development 22-23 July 2013, New York Metropolitan Area, USA Deborah A.

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Natural Language Processing for Sentiment Analysis Using the MMI Architecture

Get Smart: Smart Homes, Cars, Devices and the Web W3C Workshop on Rich Multimodal Application Development 22-23 July 2013, New York Metropolitan Area, USA

Deborah A. Dahl

Conversational Technologies Chair, W3C Multimodal Interaction Working Group

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Sentiment Analysis

Analyzing text to determine subjective

information, such as the writer’s emotional state or attitude toward the topic of the text

Useful for analyzing product reviews and

social media posts

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Demo: Classifying emotions expressed in text

Service receives text from a client (web page,

app, another web service)

Classifies the text as expressing a specific

emotion – happiness, sadness, boredom, etc., from the set of 17 “everyday categories” (Cowie et al., 1999)

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Multimodal Standards for Sentiment Analysis

EmotionML – represents emotion
EMMA – represents text interpretation
MMI Architecture – defines communication

process among components

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Roles of Client and Web Service

Client

– Captures text to be classified – Sends it to the web service for classification – Receives results – Provides feedback to the user about the results

Service

– Receives request to analyze a text – Classifies emotion of the text – Returns result to client

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Demo System

Client Web Page

HTML

Graphical display
Capture of user input

Javascript

EMMA

build EMMA documents from text
interpret returned EMMA documents

MMI Architecture

send MMI events to server over HTTP/Ajax
poll for results
receive events from server

EmotionML

interpret EmotionML documents

Interaction Manager

application-specific
controls HTML
displays results

Server

Receives MMI event

Sends “StartResponse”
Classifies text into everyday emotions
Packages result into EmotionML + EMMA
Creates MMI “DoneNotification” event
Returns event to client

Natural language modality component (RESTful Web Service on Amazon Cloud) Java

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More Information

EmotionML: http://www.w3.org/TR/emotionml/
EmotionML vocabularies: http://www.w3.org/TR/emotion-

voc/

EMMA: http://www.w3.org/TR/emma/
MMI Architecture: http://www.w3.org/TR/mmi-arch/
Demo (alpha): http://nlportal.elasticbeanstalk.amazon.com

(Works best on Firefox or Chrome)

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