We Feel Fine andSearching the Emotional Web

Sepandar D. Kamvar, Jonathan HarrisStanford University, Number 27

WSDM ’11

06 April, 2011Hye Chan, Bae

Outline Introduction Design Considerations Architecture User Interface API Discussion Conclusion

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Introduction

Sentiment analysis– The growth of the social web has led to an increased its in-

terest in Sentiment analysis

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Introduction

Sentiment analysis– Typical applications have helped consumers make purchase

decisions E.g. “thumbs up” / “thumbs down”

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happy

Introduction

Sentiment analysis– The large-scale availability of emotional text gives the ability

to better understand emotions themselves

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Introduction

We Feel Fine– A project that aims to collect the world’s emotions

(since August 2005)

– Searches the phrases “I feel” and “I am feeling”

– Identifies the “feeling” and extracts a number of demo-graphic information

– Using a series of playful interfaces, the feelings can be searched offering responses to specific questions

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Outline Introduction Design Considerations Architecture User Interface API Discussion Conclusion

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Design Considerations

Sentence-level analysis– People often express emotions at the sentence level; rarely is

an entire document about a single emotion

Indexing context– There is much useful context to an emotion outside of the

words(time, location, gender, age of the person)

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How do women feel right now?How did people in the U.S. feel on September 11th?

Design Considerations

Sentiment as the primary organizing principle– The primary aim is to understand more about emotions

themselves

De-emphasizing ranking– It is much more difficult to rank sentiment– Thousands of different expressions can be equally reason-

able responses– No ranking in We Feel Fine

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“feelings are never wrong”

Design Considerations

Emphasizing browsing and summarization– Users can gain intuition through qualitative exploration– Allowing the user to quickly get the gestalt of how a popula-

tion feels

Enabling the user to easily shift between macro and micro– Macro-level (summarization)– Micro-level (browsing)

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Micro Macro

Design Considerations

Visualizations that reflect the data– An ideal UI should reflect the subject matter

Direct Access to the Data– For both an artwork and a scientific tool, it provides a data

API for direct data access

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Outline Introduction Design Considerations Architecture User Interface API Discussion Conclusion

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Architecture

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URLServer

blog posts microblog feeds public social network msg

CrawlerList of urls

Fetched pages

Designed so that can easily add more crawling ma-

chines

Feeling In-dexer

Emotional Lexi-con

Weather Server

Image Reposi-tory

Feeling words

Location, time, date

Largest image

Architecture

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Feeling In-dexer

Image Reposi-tory

Largest image

We Feel Fine Data-

base MySQL replicated database server designed to be easily sharded by date

Feeling sentences& metadata

API Server

defines a RESTful API

Query Cache

Sentiment Mining Server

Montage Server

Architecture

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API ServerMontage Server

We Feel Fine Frontend

Montage Gallery

Third-Party Applications

Java applet

Architecture

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Outline Introduction Design Considerations Architecture User Interface API Discussion Conclusion

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User Interface

Search Panel– Allows the view to choose the sample population– Can select any combination of the following axes

Feeling, Age, Gender, Weather, Location, Date

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User Interface

Madness– A playful interface to interact with individual data items– Each particle represents a single feeling

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User Interface

Murmurs– Presents a structured environment in which to view feelings

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User Interface

Montage– Presents the feeling from a given population that contain

photographs– Any user can save a montage to the Montage Gallery

Allowing anonymous viewers to curate an exhibit of interesting images

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User Interface

Mobs– Consists of five smaller movements

feeling, gender, age, weather, location

– Aims to summarize the data set

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User Interface

Metrics– Also consists of five smaller movements– Expresses the features that are most differentially expressed

from the global average

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User Interface

Mounds– Displays every feeling in database– Each feeling is portrayed as a large bulbous mound

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User Interface

Usage Observation– Emotional Self-Awareness

the subject started talking about how she felt around the middle or end of the session

Many participants also noticed that their own emotions mirrored those of the people in the piece

– Empathy Participants reported a feeling of connection and empathy They project their own experience on to the emotions they see

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Outline Introduction Design Considerations Architecture User Interface API Discussion Conclusion

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API

2 components– RESTful API

Translates a url to a SQL query on database Returns the results in XML, HTML, CSV or plain text User can query by some conditions

– Sentiment Mining Server A set of functions that postprocess an APU query to compute

statistics– Frequency histogram, breakdown, categorize feelings, etc.

Support a wide array of uses– Has been accurate both in psychology literature and in new

hypotheses

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API

Usage Observation– The Meaning of Happiness

The co-occurrence of excited and happy feelings for younger people

The co-occurrence of peaceful and happy feelings for older peo-ple

– Hedonometer it has been built based on We Feel Fine data and the ANEW scor-

ing system

– The Emotions of Aging People’s emotions vary with age

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API

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API

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API

Usage Observation– Time-series Analyses

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API

Usage Observation– The Emotional Graph

Shows emotions that are frequently co-expressed in the same sentence

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API

Usage Observation– Artistic Purposes

Prayer Companion An installation in Denmark city hall tower A robot that mixes a drink based on the feelings returned by We

Feel Fine

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Outline Introduction Design Considerations Architecture User Interface API Discussion Conclusion

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Discussion

Unintended and broad-reaching consequences– Experiential Data Visualization

The primary responses in the user study were not cognition but affective

3 properties of EDV– Communicate insights that are often simply communicated in words

but much more powerfully communicated by example(love are easily expressed in words but more powerfully expressed by being in love)

– Focus on interaction models that encourage direct interaction with individual data items

– Focus on influencing affect rather than cognition

The design principles in section2 are useful guiding principles for EDV in general

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Discussion

Unintended and broad-reaching consequences– Crowdsourced Data Mining

Potential of crowdsourced data analysis– Over 8 million people spent an avg of 4 minutes exploring the data– Equivalent to a staff of over 50 people working full-time

Unique about We Feel Fine– Include not only statistics but detailed examples

(crowdsourced qualitative research)

Aggregating, communicating, corroborating the insights of the crowd more seamlessly is an area of future work

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Outline Introduction Design Considerations Architecture User Interface API Discussion Conclusion

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Conclusion

Item-level of exploration of data in immersive inter-face– bring experiential benefits– enable crowdsourced qualitative data analysis

Can be used to be tools to support social science re-search– Allows to run inexpensive large-scale studies to generate

data-driven hypotheses

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Thank you!!