Web Science & Technologies

University of Koblenz ▪ Landau, Germany

Topic Discovery in Unstructured Data:

The Next Generation

Christoph Kling, Sergej Sizov, Steffen Staab

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Understanding Social Media: Example Yahoo News Comments

• Many comments

• More opinions

• Commenting different (sub)topics

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Discovering topics using LDA

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more..

more..

Browse by topic

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We have: Topic-Document – All Fine?

How do we understand the topics?

Are all topics of same value?

Is there structured data to correlate?• Space• Time • Network information

We work on:• Opinions about topics• Diversity of opinions• Localisation of topics

• Time-varying topic models (Blei, Lafferty)

• ....• Geo-varying topic

models

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Geo-located social media content

Chevrolet

BMWAudi

PontiacChevrolet

Mercedes

Audi

CitroenBMW

Chevrolet

BMW

MercedesBMW

Audi

Fiat

Pontiac

CitroenPeugeot

Renault

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Geo-located social media content

Chevrolet

BMWAudi

PontiacChevrolet

Mercedes

Audi

CitroenBMW

Chevrolet

BMW

MercedesBMW

Audi

Fiat

Pontiac

CitroenPeugeot

Renault

citroenrenaultpeugeotbmw

bmwaudimercedesfiatcitroen

chevroletpontiacbmwmercedesaudi

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Related work

Chevrolet

BMWAudi

PontiacChevrolet

Mercedes

Audi

CitroenBMW

Chevrolet

BMW

MercedesBMW

Audi

Fiat

Pontiac

CitroenPeugeot

Renault

citroenrenaultpeugeotbmw

bmwaudimercedesfiatcitroen

chevroletpontiacbmwmercedesaudi

LGTA, Yin et al. 2011

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Problem

Geographical distribution of topics

Language areas Dominating religion

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Our approach

Chevrolet

BMWAudi

PontiacChevrolet

Mercedes

Audi

CitroenBMW

Chevrolet

BMW

MercedesBMW

Audi

Fiat

Pontiac

CitroenPeugeot

Renault

citroenrenaultpeugeotbmw

bmwaudimercedesfiatcitroen

chevroletpontiacbmwmercedesaudi

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Chevrolet

BMWAudi

PontiacChevrolet

Mercedes

Audi

CitroenBMW

Chevrolet

BMW

MercedesBMW

Audi

Fiat

Pontiac

CitroenPeugeot

Renault

citroenrenaultpeugeotbmw

bmwaudimercedesfiatcitroen

chevroletpontiacbmwmercedesaudi

Chevrolet

BMWAudi

PontiacChevrolet

Mercedes

Audi

CitroenBMW

Chevrolet

BMW

MercedesBMW

Audi

Fiat

Pontiac

CitroenPeugeot

Renault

citroenrenaultpeugeotbmw

bmwaudimercedesfiatcitroen

chevroletpontiacbmwmercedesaudi

Our approach

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Geographical network construction

Data points Spatial region centroids Geographical network

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Topic detection

Topic assignments

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Topic detection

Topic assignments

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Topic detection

Topic assignments

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Topic detection

Topic assignments

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Topic detection

Topic assignments

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Topic detection

Topic exchange between adjacent clusters:

Chevrolet

PontiacChevrolet

BMW

Pontiac

BMW

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Topic detection

Topic exchange between adjacent clusters:

Chevrolet

PontiacChevrolet

BMW

spatial region A

document1

spatial region B

spatial region C

Pontiac

BMW

spatial region D

AB

CD1

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Topic detection

Topic exchange between adjacent clusters:

Chevrolet

PontiacChevrolet

BMW

spatial region A

document1

spatial region B

spatial region C

Pontiac

BMW

spatial region D

AB

CD1

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Topic detection

is drawn from with equal probability

11 1

BC

A

Chevrolet

PontiacChevrolet

BMW BMW

Pontiac

A B

CD1

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Visualisation

chevrolet 0.35bmw 0.18cadillac 0.16pontiac 0.09gmc 0.07buick 0.06audi 0.05

bmw 0.29audi 0.18fiat 0.10citroen 0.09renault 0.09peugeot 0.08mercedesbenz 0.06chevrolet 0.05

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Visualisation

fiat 0.66bmw 0.10citroen 0.09renault 0.05

pontiac 0.92bmw 0.63mercedesbenz 0.17audi 0.13

renault 0.28citroen 0.22peugeot 0.15bmw 0.10audi 0.09fiat 0.07

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Topic Detection: The next generation

GeoMTD• Better understandability: „nicer regions“• Improved quality

• Better explanation of the data• Measured in terms of reduced perplexity

• about half compared to related work

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Topic Detection: The next generation

Other next generation mechanisms for understanding social media:• Opinions

• adding vocabularies with meaning (LIWC, POMS,...)

• Diversity • maximizing for spread of topics and opinions

• Author-topic-time...

Need to balance between complexity of model and sparsity of data!

Web Science & Technologies

University of Koblenz ▪ Landau, Germany

Thank you for your attention!

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References

Hierarchical Dirichlet processesby: Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. BleiIn: Journal of the American Statistical Association, Vol. 101 (2006) , p. 1566-1581.

GeoFolk: latent spatial semantics in web 2.0 social media.by: Sergej SizovIn: WSDM ACM (2010) , p. 281-290.

Geographical topic discovery and comparison.by: Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas S. HuangIn: WWW ACM (2011) , p. 247-256.

A Nonparametric Bayesian Model of Multi-Level Category Learning.by: Kevin Robert Canini, and Thomas L. GriffithsIn: AAAI AAAI Press (2011) .

Naveed, Nasir; Gottron, Thomas; Sizov, Sergej; Staab, Steffen (2012): FREuD: Feature-Centric Sentiment Diversification of Online Discussions. In: WebSci'12: Proceedings of the 4th International Conference on Web Science. ACM, 2012.

Nasir Naveed, Sergej Sizov, Steffen Staab: ATTention: Understanding Authors and Topics in Context of Temporal Evolution. European Conference on Information Retrieval 2011: 733-737. Springer, 2011.

Further papers about our work currently in preparation. Contact us if interested