Stretching the life of Twitter classifiers with time-stamped semantic graphs

Amparo Elizabeth Cano; Yulan He; Harith Alani

doi:10.1007/978-3-319-11915-1_22

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Amparo Elizabeth Cano^*, Yulan He, Harith Alani

^*Corresponding author for this work

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

Original language	English
Title of host publication	The Semantic Web – ISWC 2014
Subtitle of host publication	13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II
Editors	Peter Mika, Tania Tudorache, Abraham Bernstein, Chris Welty, et al
Place of Publication	Chem (CH)
Publisher	Springer
Pages	341-357
Number of pages	17
ISBN (Electronic)	978-3-319-11915-1
ISBN (Print)	978-3-319-11914-4
DOIs	https://doi.org/10.1007/978-3-319-11915-1_22
Publication status	Published - 31 Dec 2014
Event	13th International Semantic Web Conference - Riva del Garda, Italy Duration: 19 Oct 2014 → 23 Oct 2014

Publication series

Name	Lecture notes in computer science
Publisher	Springer
Volume	8797
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Semantic Web Conference
Abbreviated title	ISWC 2014
Country/Territory	Italy
City	Riva del Garda
Period	19/10/14 → 23/10/14

Keywords

concept drift
DBpedia
feature relevance decay
social media
topic detection

Access to Document

10.1007/978-3-319-11915-1_22

Cite this

Cano, A. E., He, Y., & Alani, H. (2014). Stretching the life of Twitter classifiers with time-stamped semantic graphs. In P. Mika, T. Tudorache, A. Bernstein, C. Welty, & et al (Eds.), The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II (pp. 341-357). (Lecture notes in computer science; Vol. 8797). Springer. https://doi.org/10.1007/978-3-319-11915-1_22

Cano, Amparo Elizabeth ; He, Yulan ; Alani, Harith. / Stretching the life of Twitter classifiers with time-stamped semantic graphs. The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II. editor / Peter Mika ; Tania Tudorache ; Abraham Bernstein ; Chris Welty ; et al. Chem (CH) : Springer, 2014. pp. 341-357 (Lecture notes in computer science).

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title = "Stretching the life of Twitter classifiers with time-stamped semantic graphs",

abstract = "Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.",

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author = "Cano, {Amparo Elizabeth} and Yulan He and Harith Alani",

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Cano, AE, He, Y & Alani, H 2014, Stretching the life of Twitter classifiers with time-stamped semantic graphs. in P Mika, T Tudorache, A Bernstein, C Welty & et al (eds), The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II. Lecture notes in computer science, vol. 8797, Springer, Chem (CH), pp. 341-357, 13th International Semantic Web Conference , Riva del Garda, Italy, 19/10/14. https://doi.org/10.1007/978-3-319-11915-1_22

Stretching the life of Twitter classifiers with time-stamped semantic graphs. / Cano, Amparo Elizabeth; He, Yulan; Alani, Harith.
The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II. ed. / Peter Mika; Tania Tudorache; Abraham Bernstein; Chris Welty; et al. Chem (CH): Springer, 2014. p. 341-357 (Lecture notes in computer science; Vol. 8797).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Stretching the life of Twitter classifiers with time-stamped semantic graphs

AU - Cano, Amparo Elizabeth

AU - He, Yulan

AU - Alani, Harith

PY - 2014/12/31

Y1 - 2014/12/31

N2 - Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

AB - Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

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KW - DBpedia

KW - feature relevance decay

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KW - topic detection

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U2 - 10.1007/978-3-319-11915-1_22

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M3 - Conference publication

AN - SCOPUS:84910093709

SN - 978-3-319-11914-4

T3 - Lecture notes in computer science

SP - 341

EP - 357

BT - The Semantic Web – ISWC 2014

A2 - Mika, Peter

A2 - Tudorache, Tania

A2 - Bernstein, Abraham

A2 - Welty, Chris

A2 - et al,

PB - Springer

CY - Chem (CH)

T2 - 13th International Semantic Web Conference

Y2 - 19 October 2014 through 23 October 2014

ER -

Cano AE, He Y, Alani H. Stretching the life of Twitter classifiers with time-stamped semantic graphs. In Mika P, Tudorache T, Bernstein A, Welty C, et al, editors, The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II. Chem (CH): Springer. 2014. p. 341-357. (Lecture notes in computer science). doi: 10.1007/978-3-319-11915-1_22

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Abstract

Publication series

Conference

Keywords

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