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

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.

Finding Diachronic Like‐Minded Users

“…Applying topic modeling methods such as LDA and ToT to extract topics from tweets might suffer from the sparsity problem (Sriram et al, ; Cheng et al, ), because they are designed for regular documents and not short, noisy, and informal texts like tweets. As suggested in Varga et al (), to obtain better topics from Twitter without modifying the standard topic detection methods, we annotate each tweet with concepts defined in Wikipedia using an existing semantic annotator. We see each concept as a term in the set .…”

Advances in Machine Learning for the Behavioral Sciences

“…External information can be particularly useful in domains where the available documents are short and do not thus contain much information. To this end, Varga, Cano Basave, Rowe, Ciravegna, and He (2014) report significant improvement in performance when the content of tweets is linked to knowledge graphs as opposed to lexical-only content contained in the input tweets.…”

Semantic analysis on social networks: A survey

“…In a study addressing the problem of classifying microposts, a novel meta-graph was presented to ensure a more fine-grained classification of concepts providing a set of novel semantic features. 117 In another study, Yang et al proposed a new and unsupervised topic model called Trend-Sensitive-Latent Dirichlet Allocation (TS-LDA) that can effectively extract latent topics from contents and tried to find interesting tweets based on topic identification by modeling temporal trends on Twitter over time. 118 LDA is a fundamental method of topic semantic modeling that discovers latent topics from the documents.…”