header

Linked Knowledge Sources for Topic Classification of Microposts: A Semantic Graph-Based Approach

24 Pages Posted: 3 Jul 2018 First Look: Accepted

See all articles by Andrea Varga

Andrea Varga

University of Sheffield - The OAK Group (Organisations, Information and Knowledge Group)

Amparo E. Cano

Aston University - School of Engineering and Applied Science

Matthew Rowe

The Open University - Knowledge Media Institute

Fabio Ciravegna

University of Sheffield - The OAK Group (Organisations, Information and Knowledge Group)

Yulan He

Aston University - School of Engineering and Applied Science

Abstract

Short text messages, a.k.a microposts (e.g., tweets), have proven to be an effective channel for revealing information about trends and events, ranging from those related to disaster (e.g., Hurricane Sandy) to those related to violence (e.g., Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond.

In this work we study the problem of topic classification (TC) of microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information.

In order to provide contextual information to microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of microposts with features extracted only from the microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of microposts.

Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and microposts at a conceptual level, considering the enriched representation of these documents.

Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures.

Keywords: linked knowledge sources, semantic concept graphs, topic classification

Suggested Citation

Varga, Andrea and Cano, Amparo E. and Rowe, Matthew and Ciravegna, Fabio and He, Yulan, Linked Knowledge Sources for Topic Classification of Microposts: A Semantic Graph-Based Approach (2014). Journal of Web Semantics First Look. Available at SSRN: https://ssrn.com/abstract=3199099 or http://dx.doi.org/10.2139/ssrn.3199099

Andrea Varga (Contact Author)

University of Sheffield - The OAK Group (Organisations, Information and Knowledge Group) ( email )

Regent Court, 211 Portobello
Sheffield, +44 114 222 1800
United Kingdom

Amparo E. Cano

Aston University - School of Engineering and Applied Science

Aston Triangle
Birmingham, B4 7ET
United Kingdom

Matthew Rowe

The Open University - Knowledge Media Institute ( email )

Walton Hall
Milton Keynes
United Kingdom

Fabio Ciravegna

University of Sheffield - The OAK Group (Organisations, Information and Knowledge Group)

Regent Court, 211 Portobello
Sheffield, +44 114 222 1800
United Kingdom

Yulan He

Aston University - School of Engineering and Applied Science

Aston Triangle
Birmingham, B4 7ET
United Kingdom

Register to save articles to
your library

Register

Paper statistics

Abstract Views
2,108
Downloads
3