The document describes the creation of a Spanish corpus for sentiment analysis towards brands. The corpus was built by collecting tweets related to major brands in sectors like food, automotive, banking, etc. The tweets were then sifted, tagged with emotions like hate, sadness, and happiness, and transformed into linked data using ontologies. The final corpus contains over 4,500 tweets annotated with emotions to analyze consumer sentiment towards brands in Spanish. It aims to fill a gap in resources for Spanish sentiment analysis and connect the text to information about brands. Future work includes fully annotating the corpus and adding more tweets and semantic annotations.

1. Spanish Corpus for Sentiment
Analysis towards Brands
María Navas-Loro, Víctor Rodríguez-Doncel,
Idafen Santana-Perez, Alberto Sánchez
Technical University of Madrid
mnavas@fi.upm.es
SPECOM, 14th September 2017

2. INTRODUCTION
Spanish Sentiment Analysis
2

3. Introduction
Sentiment Analysis on Social Networks allows companies
to know better client opinions.

4. Main problem
Sentiment Analysis,
specially in Spanish, just
focuses on polarity...
… also due to Twitter policies,
there is not much annotated
training available…

5. Main problem
And even though we can find corpora in several fields,
such as the medical or the touristic, or more general
opinions...
… there is nothing for
opinion towards brands
in Spanish!

6. ANALYSIS PROPOSAL
Designed schema
6

7. Who we are

8. Analysis proposal
Post
Meaningful
Brands
Marketing
Mix
Sentiment
Analysis
Purchase
Funnel
Marketplace
Personal
Wellbeing
Collective
Wellbeing
AwarenessEvaluation
Purchase
Postpurchase
Review
Product
Price
Promotion
Place
Hate / Love
Satisfaction / Dissatisfaction
Hapiness / Sadness
Trust / Fear

9. Analysis proposal
Post
Meaningful
Brands
Marketing
Mix
Sentiment
Analysis
Purchase
Funnel
Marketplace
Personal
Wellbeing
Collective
Wellbeing
AwarenessEvaluation
Purchase
Postpurchase
Review
Product
Price
Promotion
Place
Hate / Love
Satisfaction / Dissatisfaction
Hapiness / Sadness
Trust / Fear
Purchase
Funnel
AwarenessEvaluation
Purchase
Postpurchase
Review
When?

10. Analysis proposal
Post
Meaningful
Brands
Marketing
Mix
Sentiment
Analysis
Purchase
Funnel
Marketplace
Personal
Wellbeing
Collective
Wellbeing
AwarenessEvaluation
Purchase
Postpurchase
Review
Product
Price
Promotion
Place
Hate / Love
Satisfaction / Dissatisfaction
Hapiness / Sadness
Trust / Fear
Meaningful
Brands
Marketplace
Personal
Wellbeing
Collective
Wellbeing
Where?

11. Post
Meaningful
Brands
Marketing
Mix
Sentiment
Analysis
Purchase
Funnel
Marketplace
Personal
Wellbeing
Collective
Wellbeing
AwarenessEvaluation
Purchase
Postpurchase
Review
Product
Price
Promotion
Place
Hate / Love
Satisfaction / Dissatisfaction
Hapiness / Sadness
Trust / Fear
Sentiment
Analysis
Hate / Love
Satisfaction / Dissatisfaction
Hapiness / Sadness
Trust / Fear
Emotion?
Analysis proposal

12. Analysis proposal
Post
Meaningful
Brands
Marketing
Mix
Sentiment
Analysis
Purchase
Funnel
Marketplace
Personal
Wellbeing
Collective
Wellbeing
AwarenessEvaluation
Purchase
Postpurchase
Review
Product
Price
Promotion
Place
Hate / Love
Satisfaction / Dissatisfaction
Hapiness / Sadness
Trust / Fear
What?
Marketing
MixProduct
Price
Promotion
Place

13. Analysis proposal
Post
Meaningful
Brands
Marketing
Mix
Sentiment
Analysis
Purchase
Funnel
Marketplace
Personal
Wellbeing
Collective
Wellbeing
AwarenessEvaluation
Purchase
Postpurchase
Review
Product
Price
Promotion
Place
Hate / Love
Satisfaction / Dissatisfaction
Hapiness / Sadness
Trust / Fear
Purchase
Funnel
AwarenessEvaluation
Purchase
Postpurchase
Review
Marketing
MixProduct
Price
Promotion
Place
Sentiment
Analysis
Hate / Love
Satisfaction / Dissatisfaction
Hapiness / Sadness
Trust / Fear
Meaningful
Brands
Marketplace
Personal
Wellbeing
Collective
Wellbeing
When?
Where?
Emotion?
What?

14. LPS BIGGER project

15. BUILDING PROCESS
Spanish Corpus for Sentiment Analysis towards Brands
15

16. Corpus building
1. Selection
of the brands
2. Acquisition
of tweets
3. Sifting 4. Tagging 5. Transformation
Sector Brand
BEVERAGES Cruzcampo, Heineken, Estrella Galicia, Mahou
AUTOMOTIVE Citroën, Fiat, Hyundai, Kia, Peugeot, Toyota
BANKING
Bankia, Bankinter, BBVA, Sabadell, ING, La
Caixa/Caixabank, Santander
FOOD Auchan, Bimbo, Hacendado, Milka, Pascual, Puleva
RETAIL
Alcampo, Carrefour, Decathlon, Ikea, Leroy Merlin,
Mediamarkt, Mercadona
TELECOM Amena, Lowi, Movistar, Orange, Vodafone, Yoigo
SPORTS Adidas, Nike, Reebok

17. Corpus building
2. Acquisition
of tweets
1. Selection of
the brands
3. Sifting 4. Tagging 5. Transformation
• Collected from Twitter between the 1st and the 7th
February 2017.
• Using keywords just related to the name of the
brands.
• Using just a filter, for Spanish tweets.
• Avoiding retweets.
• At the end of this step, there remainded more tan
23,000 tweets.

18. Corpus building
3. Sifting
1. Selection of
the brands
4. Tagging 5. Transformation
• Manual and automatic screening:
• Repeated tweets.
• The same tweet changing the URL.
• No real Brand (polysemous).
• Other languages…
• We obtain the final 4548 tweets.
2. Acquisition
of tweets

19. Corpus building
4. Tagging
1. Selection of
the brands
5. Transformation
• Three taggers for BEVERAGES, one for the whole corpus.
• Tags are per post, being possible have different emotions.
• Specific criteria was given to the taggers (available with
the corpus), along with information on interagreement and
Kappas. Example of criteria:
HAPPINESS is only given to products already acquired, not to future
purchases.
If a desired product is not found, SATISFACTION and SADNESS are
tagged.
2. Acquisition
of tweets
3. Sifting

20. Corpus building
5. Transformation
1. Selection of
the brands
• The corpus is represented as linked data, reusing
ontologies and also creating new classes:
• Marl and Onyx.
• SIOC.
• GoodRelations.
• It is also linked to external databases such as:
• Thomson Reuters’ PermID.
• DBpedia.
2. Acquisition
of tweets
3. Sifting 4. Tagging

21. SAB CORPUS
Spanish Corpus for Sentiment Analysis towards Brands
21

22. Final corpus statistics
Sector HATE SADNESS FEAR DISSATISF. NC2 SATISF. TRUST HAPPINESS LOVE
WITH
EMOTION
FOOD 5 4 0 28 181 153 149 50 44 367
AUTOMOTIVE 0 1 5 11 508 33 17 6 5 551
BANKING 32 6 92 146 561 8 3 0 0 712
BEVERAGES 15 8 5 131 253 302 225 51 53 686
SPORTS 16 17 2 87 430 123 78 32 74 653
RETAIL 27 10 13 100 1057 119 118 31 28 1328
TELECOM 32 2 0 73 152 21 15 8 3 249
TOTAL 127 48 117 576 3142 759 605 178 207 4546

23. SAB corpus connections
SAB corpus connects to external resources and uses
a proper vocabulary along with reusing
several ontologies.
Post
Meaningful
Brands
Marketing
Mix
Sentiment
Analysis
Purchase
Funnel
Marketplace
Personal
Wellbeing
Collective
Wellbeing
AwarenessEvaluation
Purchase
Postpurchase
Review
Product
Price
Promotion
Place
Hate / Love
Satisfaction / Dissatisfaction
Hapiness / Sadness
Trust / Fear

24. Example of a post
lps:826812979421257730 a sioc:Post ;
sioc:id "826812979421257730" ;
sioc:content "Ya me quede sin credito?? Hace 3 dias
tengo credito nomas... Movistar y la concha de tu
hermana"@es ;
marl:describesObject lps:Movistar ;
lps:isInPurchaseFunnel lps:postPurchase;
lps:hasMarketingMix lps:price;
lps:hasMeaningfulBrand lps:marketplace;
onyx:hasEmotion lps:hate, lps:dissatisfaccion ;
marl:hasPolarity marl:negative ;
marl:forDomain "TELCO" .
lps:hate a onyx:Emotion ;
rdfs:label "odio"@es, "hate"@en .
lps:dissatisfaction a onyx:Emotion ;
rdfs:label "insatisfaccion"@es, "dissatisfaction"@en .

25. Example of information for a brand and a company
lps:Movistar a gr:Brand ;
rdfs:seeAlso <http://dbpedia.org/resource/Movistar>
;
rdfs:label "Movistar" .
lps:1-5000062703 a gr:Business ;
rdfs:label "Telefonica de Espana, S.A.U.";
rdfs:seeAlso
<https://opencorporates.com/companies/es/82018474> ;
owl:sameAs permid:1-5000062703 .

26. CONCLUSIONS
Contributions and future work
26

27. Contributions and future lines
Some contributions of the SAB corpus:
• It covers a gap in the Spanish Sentiment Analysis.
• It offers a representation for Sentiment Analysis towards
Brands independent of the language.
• It offers Linked Data information that:
• Prevent corpus to be outdated (changes in names, for instance).
• Offer data related to brands beyond the text (CEOs…)
Future lines:
• Full annotation of all the aspects.
• More links.
• More tweets.
• Semantic annotation of emotional keywords.

28. Bibliography
Breslin, J.G., Decker, S., et al.: Sioc: an approach to connect web-
based communities. International Journal of Web Based
Communities 2(2), 133-142 (2006)
Sanchez Rada, J.F., Torres, M., et al.: A linked data approach to
sentiment and emotion analysis of twitter in the financial domain.
In: 2nd International Workshop on Finance and Economics on the
Semantic Web (2014)
Hepp, M.: Goodrelations: An ontology for describing products and
services offers on the web. In: International Conference on
Knowledge Engineering and Knowledge Management. pp. 329-
346. Springer (2008)
Thomson Reuters’ PermID: https://permid.org/
Dbpedia: http://dbpedia.org/

29. Link to the corpus
http://sabcorpus.linkeddata.es/
Thank you for your attention

30. Spanish Corpus for Sentiment
Analysis towards Brands
María Navas-Loro, Víctor Rodríguez-Doncel,
Idafen Santana-Perez, Alberto Sánchez
Technical University of Madrid
mnavas@fi.upm.es
SPECOM, 14th September 2017