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Kick-Off RENDER Project

Kalsruhe, October27st 2010

Telefónica I+D

Telefónica I+D User Modeling Analytical Models

Index

Telefónica Case StudyOverviewData SourcesResultsData Key PointsData Considerations

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02

Área: Lorem ipsumRazón Social: TelefónicaTelefónica I+D User Modeling Analytical Models

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Annex A: Twitter Analysis Examples02

Case Study

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Overview

�RENDER will provide means to enable Telefónica to assess theincoming requests, complaints and concerns, identify opinions,viewpoints, trends and tendencies, and take feasible actions basedthereupon.


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Data Sources

Call Centers

Contacts

Web Customer

Portal Messages

Surveys (Shops &

Market Research)


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Public Forums

Comments

Corporate Forums

Comments

Twitter Entries

Data Sources

• Twitter data collection›

�Amounts of Data• Data in corporate channels

› Movistar España

› O2 UK and O2 Ireland

• Data in public channels› Open forums


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› 600.000 tweets per day (1% total)

› By geolocation

› 23.000 tweets/day in UK

› 5.000 tweets/day in Spain

› 900 tweets/day in Ireland

› By topic

› 3.300 tweets/day speaking about O2

› 3.200 tweets/day speaking about Movistar

› 800 tweets/day speaking about Telefónica

Results�What do we want to achieve in this project?

• To apply of NLP, data mining, web mining, and machine learningtechniques in order to discover and analyze in‐depth large streams ofdata from various sources, across multiple (natural) languages, and acomprehensive opinion model covering intensity, biases and factcoverage.

�Key aspects• Management of data source

›


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› Internal Data Vs. External Data

• Processing of the data bias› Customer Vs. Potential customer

› Non-experimented Vs. Advanced users

• Vision of segmented opinion› Individual Opinion Vs. Global Opinion

• Identification of the subjectivity in the opinions› Positive, Negative and Neutral Opinions

• Knowledge of opinion geolocalization (Twitter entries)

Internal dataInternal dataInternal data

Data Key Points

Web Customer Portal

Corporate Forums

Call Center

Customers Customers Customers


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Customers Customers Customers

Objective / Subjective



No possible segmentation

Possible segmentation


Possible localization Possible localization (with user account)

Language identifiedLanguage not

identifiedLanguage not

identified

Possible localization (with user account)

Offline users Online users Online users

Surveys (shops & market research)

Public Forum Twitter Entries

Data Key Points

Internal data External dataExternal data

Customers or Potential Customers or Potential Customers or Potential


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Customers or Potential Customers







Not identified language Identified language

Not identified language

Possible localizationNot always possible

localizationNot possible localization

Offline usersOnline users Advanced online users



Data ConsiderationsCall Center

Only interaction customer with the CRM.

Technical Limitations due to working with recordings:- Speech recognition - User/Operator in the same channel (User diarization)

Formal language.

The transcriptions have not mistakes as unknown words and symbols (only recognition errors).


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channel (User diarization)

High difficulty data acquisition.

Customers don’t speak freely, it’s a formal dialogue.

The topics list is limited, the issues are defined.

The most of calls don’t express opinion, are only questions and complaints.

Data ConsiderationsWeb Customer Portal


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Data Considerations Web Customer Portal

Text sentences can have errors (grammar, vocabulary…)

Customers don’t write freely, it’s a formal message.

Formal language.

The technical limitations will only be the challenge of the Opinion Mining.


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Only interaction customer with the CRM.

Medium difficulty data acquisition.

The list of topics is limited, the issues are defined.

The most of comments don’t express opinion, only questions and complaints.

Data Considerations Forums Comments

�Corporate forum


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�Public forum


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Informal language.

Transcriptions can have errors (grammar, vocabulary…)

Only Interaction between

Customers write in complete freedom.

The comments can express opinion.


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Only Interaction between customers (Public Forums)


opinion.

The list of topics is unlimited, customers can open any new issue.

Interaction customer-enterprise and between customers (Corporate Forums)


Data Considerations Surveys (shops & market research)


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Data Considerations Surveys (shops & market research)

The list of topics is limited.

Only Interaction customer-enterprise

Formal language.



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Transcriptions without errors and natural language.


Data Considerations Twitter Entries


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Data Considerations Twitter Entries

Informal language.

Transcriptions can have errors (grammar, vocabulary…)

Low difficulty data acquisition.


Customers write in complete


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The list of topics is unlimited, customers can open any new issue.

Interaction customer-enterprise and between customers.


Annex A: Twitter AnalysisExamples

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Examples


Twitter Analysis Examples�Current opinion mining projects in Twitter with no interesting results

• TwitrratrO2 can’t be

searched because it has only two characters. ��

There’s only 4 results for ‘O2

Ireland’


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Ireland’

The only 4 results are classified as

neutral

This comment is really negative!

Twitter Analysis Examples�Current opinion mining projects in Twitter with no interesting results

• Tweetfeel

It’s possible to search O2, but…

…the results are

bad!


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Sometimes it’s well

classified

Sometimes the word

doesn’t exist

And the rest it’s bad

classified or identified!

Twitter Analysis Examples�Current projects with no interesting results

• Tweetfeel

In this case it’s possible to search

O2 Ireland...

…but it’s not


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�There is still much work to do…

possible as following words

There are only 4 results, and 3 are RT (retweeting)

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