Identifying Topics in Social Media Posts using DBpedia

NEM Summit – 28 Sept. 2011
Identifying Topics in Social Media
Posts using DBpedia
Óscar Muñoz-García, Manuel de la Higuera Hernández, Carlos Navarro (Havas Media)
Andrés García-Silva, Óscar Corcho (Ontology Engineering Group - UPM)

Contents
Identifying Topics in Social Media Posts using DBpedia ⎢2
 Introduction
 Related Work
 Description of the Method
 Evaluation
 Conclusions

Identifying Topics in Social Media Posts using DBpedia
Introduction

Introduction
 Topic Identification
 “The task of identifying the central ideas in a text” [Chin-Yew Lin, 1995]
 Applications of Topic Identification for Social Media
 Automatically summarising the content published in a channel.
 Mining the interest of a given user.
 etc…
 Benefits for Advertising Companies
 To focus the advertisement actions to the appropriate channels.
 To serve ads to the users based in their interest.

Introduction
 Difficulties of Topic Identification in Social Media
 Different channels with heterogeneous texts
l Different lengths
 From short sentences on Twitter to medium-size articles in blogs
l Misspellings
 Posts completely written in uppercase (or lowercase) letters
 Makes difficult the detection of proper nouns.
 In Spanish, absence /presence of an accent in a word different meanings
 “té” = “tea” (common noun)
 “te” = “you” (personal pronoun)
l Use of set phrases
 E.g., “too many cooks spoil the broth” (if too many people try to take charge at
a task, the end product might be ruined)
 E.g., “rain cats and dogs” (rains heavily)
 It is important to take into account the context of the post

Introduction
 Why DBpedia?
 DBpedia is a structured Semantic Web representation of Wikipedia
l Wikipedia is maintained by thousands of editors
l Wikipedia evolves and adapts as knowledge changes [Syed et al, 2008]
 Each topic identified is mapped with a DBpedia resource
l E.g., The URI http://dbpedia.org/resource/Turin
 Represents the city of Torino
 Has about 45 attributes defined (population, area, latitude, longitude, etc.)
 Has labels and definitions in 14 different languages.
 It is linked with many semantic entities
 E.g. Birth place of Amedeo Avogadro: http://dbpedia.org/resource/Amedeo_Avogadro
 It is linked with its Wikipedia article: http://en.wikipedia.org/wiki/Torino
 It is a nucleus for the Web of Data [Bizer et al, 2009]
l Data published on the Web according to Tim Berners-Lee’s Linked Data principles.
l Several billion RDF triples (i.e. facts)
l Multi-domain datasets (geographic information, people, companies, online
communities, etc…)

Related Work

Related Work
 Wikipedia has been exploited for the following tasks:
 Topic identification and text categorization
l [Bodo et al, 2007], [Coursey et al, 2009], [Gabrilovich et al., 2006], [Syed et
al, 2008], [Schonhofen, 2009]
 Semantic Relatedness between fragments of text
l [Gabrilovich et al, 2007]
 Keyword Extraction
l [Mihalcea et al, 2007]
 Word sense disambiguation
l [Mihalcea, 2007]

Related Work
 Uses of Wikipedia data-structure:
 Relating words in text with articles using article title information
l [Schonhofen, 2009]
 Exploiting anchor text in links
l [Coursey et al, 2009] [Mihalcea et al, 2007] [Mihalcea, 2007]
 Exploiting the whole articles
l [Syed et al, 2008] [Gabrilovich, 2007]
 Exploiting categories to measure relatedness between articles
l [Coursey et al, 2009] [Syed et al, 2008]
 Exploiting disambiguation pages and redirection links to select
candidate senses and alternative labels
l [Mendelyan et al, 2008]

Related Work
 Supervised learning methods
l [Bodo et al, 2007] [Gabrilovich et al, 2006] [Mendelyan et al, 2008]
 Unsupervised techniques
 Based on a Vector Space Model
l [Schonhofen, 2009]
 Based in a Graph
l [Coursey et al, 2009] [Syed et al, 2008]
 Combined methods (supervised and unsupervised)
 Based on a Vector Space Model
l [Mihalcea et al, 2007]

Related Work
 Our approach
 Exploits titles, disambiguation pages, redirection links and article
text to select candidate senses and alternative labels
 Uses an unsupervised method
 Uses a vector space model
 Main benefit in comparison with previous approaches:
 The interlinking of social media posts with the Web of data through
DBpedia resources

Description of the Method

Input
Part-of-
speech
tagging
• “torino”, “art”, “media”, “user”, “cloud”
Topic
Recognition
• http://dbpedia.org/resource/Turin
• http://dbpedia.org/resource/Art
• http://dbpedia.org/resource/User_(computing)
Language
Filtering
• “Torino”, “arte”, “utente”, “mezzo di comunicazione di massa”, ...

 Part-of-speech tagging
 Wp = w1,w2, ..., wn list of lexical units contained in the post
 lexcat(w) lexical category of the lexical unit w
 lemma(w) lemma of w
 L = {common noun, proper noun, acronym…} meaningful lexical categories
that we consider
 = {“RT”, “/cc”, “;)”, …} stop words (lemmas excluded)
 Kp = k1,k2, …, kn list of keywords with meaning

 Part-of-speech tagging example
Input
• But a hardware problem is more likely, especially if
you use the phone a lot while eating. The
Blackberry's tiny trackball could be suffering the
same accumulation of gunk and grime that can
plague a computer mouse that still uses a rubber
ball on the underside to roll around the desk.
Part-of-speech
tagging
• Blackberry, phone, trackball, computer,
problem, grime, hardware, mouse, desk,
rubber ball, gunk

 Topic Recognition (Sem4Tags [García-Silva et al, 2010])
POS
tagging
• Blackberry, phone, trackball, computer, problem, grime, hardware,
mouse, desk, rubber ball, gunk
Context
Selection
• Blackberry, {phone, hardware, trackball, mouse}
• Computer, {hardware, mouse, problem, desk}
• …
Disambiguation
• http://dbpedia.org/resource/BlackBerry
• http://dbpedia.org/resource/Computer

 Context Selection
 For each keyword, a set of up to 4 related keywords that will help to
disambiguate the its meaning
 4 is the number of words above which the context does not add more resolving
power to disambiguation [Kaplan, 1955]
 We compute semantic relatedness (active context) taking into account the
co-ocurrence of words in web pages [Gracia et al, 2009]
Keyword Relatedness Keyword Relatedness
phone 0.347 hardware 0.347
trackball 0.311 mouse 0.311
computer 0.288 desk 0.287
problem 0.246 rubber ball 0.246
grime 0.190 gunk 0.168
Active context selection for blackberry keyword

 Disambiguation Criteria
 OPTION 1: Most frequent sense for the ambiguous word
l Determined by Wikipedia editors (the first link in a disambiguation page)
 OPTION 2: Vector space model
1. A vector containing the keyword and its context
2. A vector containing top N terms is created from each candidate sense is created using
TF-IDF (Term Frequency and Inverse Document Frequency)
3. The cosine similarity is used to determine which vectorised sense is more similar to
the vector associated to the keyword
DBpedia resource Definition Similarity
BlackBerry
Is a line of mobile e-mail and
smartphone
0.224
Blackberry is an edible fruit 0.15
BlackBerry_(song) is a song by the Black Crowes 0.0
BlackBerry_Township,
_Itasca_County,
_Minnesota
Is a towship in … Itasca County 0.0

 Language Filtering
 Tp = t1,t2, ..., tn set of topics identified
 l language to filter
 Labels(t) set of labels associated to a given topic (value of rdfs:label
property)
 lang(b) language of a given label
 Tl
p set of topics with labels in language l

Evaluation

Evaluation
 Evaluated with a corpora of 10,000 posts in Spanish extracted from
 Blogs
 Forums
 Microblogs (e.g., Twitter)
 Social networks (e.g., Facebook, MySpace, LinkedIn and Xing)
 Review sites (e.g. , Ciao and Dooyoo)
 Audiovisual sites (e.g., YouTube and Flickr)
 News publising sites (e.g., elpais.com, elmundo.es)
 Others (web pages not classified in the categories above)
 Variants evaluated
1. Without considering any context
l Default Wikipedia sense assigned for a given keyword
2. Considering as context all the other keywords found in the same post
3. Active context selection technique
l Selecting the 4 most relevant topics from the keywords in the same post

Evaluation
 Coverage
 Part-of-speech tagging: nearly 100%
 Topic recognition: over 90% for almost all the cases
 After language filtering coverage is reduced in about 10% because not all
DBpedia resources have a label defined for Spanish language
Blogs Forums Microblogs Social Networks Others Reviews Audiovisual News Overall
POS Tagging 99.63% 96,64% 99.01% 98.14% 98.77% 98.20% 97.20% 99.62% 98.32%
Topic identification
Without context 96.7% 87.68% 94.22% 93.54% 92.71% 88.81% 90.29% 96.67% 92.35%
With context 96.64% 93.07% 95.54% 94.99% 95.13% 92.67% 97.41% 98.54% 95.02%
Active context 99.24% 89.71% 94.43% 96.40% 94.75% 93.81% 92.23% 97.4% 94.72%
Topic identification after language filtering
Without context 91.21% 79.04% 87.54% 82.64% 86.93% 70.15% 82.52% 90.71% 82.74%
With context 88.43% 80.84% 86.31% 85.24% 88.72% 76.19% 89.66% 92.46% 84.85%
Active context 89.69% 80.51% 86.51% 86.78% 89.78% 75.59% 80.58% 90.54% 84.73%

Evaluation
 Precision
 Evaluated a random sample of 1,816 posts (18,16%)
 47 human evaluator
 Each post and topics identified shown to 3 different evaluators
 Evaluation options:
1. The topic is not related with the post
2. The topic is somehow related with the post
3. The topic is closely related with the post
4. The evaluator has not enough information for taking a decision
 Fleiss’ kappa test
l Strength of agreement for 2 evaluators = 0.826 (very good)
l Strength of agreement for 3 evaluators = 0.493 (moderate)

Evaluation

Evaluation
 Precision Results
 Precision depends on the channel
l From 59.19% for social networks
 More misspellings
 More common nouns
l To 88.89% for review sites
 Concrete products and brands
 Proper nouns tend to have a Wikipedia entry
 Context selection criteria also depends on the channel
l Active context selection better for microblogs and review sites
l Considering all the post keywords as context better for blogs
l Without context selection is better for the rest of the cases (almost all the channels)
 Naïve default sense selection is effective

Conclusions

Conclusions
 We have achieved good results of coverage
 The precision depends on the channel (better for review sites,
worst for social networks)
 With respect to considering context or not, there is not a variant that
provide the best results for all the channels.
 Future lines of work:
 Improve Natural Language Processing
l Dealing with slang
l Detect set phrases
l Improve n-gram detection
l Dealing with microblogs’ specifics (e.g., hashtag expansion)
 Combine broad-domain topic identification with knowledge about
specific domains
l Use of domain ontologies in combination with DBpedia ontology

Thank you!
oscar.munoz@havasmedia.com

Identifying Topics in Social Media Posts using DBpedia

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