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OpeNER: Open Tools to Perform Natural Language Processing on

Accommodation ReviewsAitor García-Pablos, Montse Cuadros, María Teresa Linaza

Vicomtech-IK4, Spainagarciap,mcuadros,mtlinaza@vicomtech.org

http://www.vicomtech.org

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Summary

• Introduction• The OpeNER project

– Objective– Architecture

• An example• Some results• Conclusions

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Introduction

• Web 2.0 and Social Networks have changed the way the customer information flows

• These new channels generate high amounts of information related to the following issues– Preferences of potential customers– Requests/complaints from current customers– Feedback from past customers

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Introduction (2)

• However, it is not feasible to manage this information manually– Too time consuming– Large investment to track all sources from the

Web

• Computers can help processing texts – Detection of mentions of certain entities– Classification of the reviews regarding their

polarity

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Introduction (3)

• The so-called “opinion mining” and “sentiment analysis” tools offer these type of services

• Main current limitations– Most of them are not free– They are too complex– It is not obvious how to integrate them into a

real service

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OpeNER project

• OpeNER is a 7th Framework Programme European project which aims at providing a set of Open Source tools to perform text processing tasks– Named Entity Recognition, sentiment analysis, etc.– For six languages– Free and Open Source– Modular and easy to integrate

www.opener-project.eu

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OpeNER project (2)

• Basic tools to allow end users and/or SMEs building a customized products or services with textual content analysis– Free tool– Easy to integrate to ease building upon it– Open Source to customize the code

www.opener-project.eu

https://github.com/opener-projecthttps://github.com/opener-project

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OpeNER architecture

KAF (Bosma et al. 2009)

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A practical example

“I have been at Albergo Acquarello hotel at Lugano and I liked the beautiful decoration. The rooms were very comfortable. On the other hand, the restaurant was really expensive.”

An hypothetic customer review

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A practical example (2)

Named Entity Recognition, Classification and Linking:

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A practical example (3)

Sentiment/Polarity detection:

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A practical example (4)

Opinion detection using:

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Some evaluation resultsTool Language Precision Recall F-Score Method Dataset

Opinion detector

en 85,52% 58,45% 69,44%CRF + SVM

OpeNER manual hotel annotations

Opinion detector

nl 82,8% 51,77% 63,71%CRF + SVM

OpeNER manual hotel annotations

Opinion detector

de 75,64% 48,88% 59,38%CRF + SVM

OpeNER manual hotel annotations

Opinion detector

es 74,41% 46,55% 57,27%CRF + SVM

OpeNER manual hotel annotations

Opinion detector

it 65,47% 40,39% 49,96%CRF + SVM

OpeNER manual hotel annotations

Opinion detector

fr 70,94% 46,28% 56,02%CRF + SVM

OpeNER manual hotel annotations

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Tour-pedia

Concept application: Tour-pedia,Developed at the CNR Pisa, within the OpeNER project

www.tour-pedia.org/gui/demo/

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Tour-pedia (2)

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Conclusions

• Web 2.0 enables a valuable customer communications channels that require technology to be efficiently processed

• There are some tools already in the market and in the academia, but they are usually difficult or expensive to use

• OpeNER provides with some of these technologies, free, open source, and easy to use and integrate

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Thank you for your attention!Any question?

www.opener-project.euhttp://www.vicomtech.org