POPSTAR at RepLab 2013: Name ambiguity resolution on Twitter

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Saleiro, P (Author) Other; Rei, L (Author) Other; Pasquali, A (Author) Other; Carlos Soares (Author) FEUP; teixeira, j (Author) FEUP; Pinto, F (Author) Other; Nozari, M (Author) Other; Felix, C (Author) Other; Strecht, P (Author) Other

Conference proceedings International

Title: CEUR Workshop Proceedings

3rd Workshop on Ubiquitous Data Mining, UDM 2013 - Co-located with the 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

3 August 2013

Indexing

Scopus - 5 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: P-00A-4K8

Abstract (EN): Filtering tweets relevant to a given entity is an important task for online reputation management systems. This contributes to a reliable analysis of opinions and trends regarding a given entity. In this paper we describe our participation at the Filtering Task of RepLab 2013. The goal of the competition is to classify a tweet as relevant or not relevant to a given entity. To address this task we studied a large set of features that can be generated to describe the relationship between an entity and a tweet. We explored different learning algorithms as well as, different types of features: text, keyword similarity scores between enti-ties metadata and tweets, Freebase entity graph and Wikipedia. The test set of the competition comprises more than 90000 tweets of 61 entities of four distinct categories: automotive, banking, universities and music. Results show that our approach is able to achieve a Reliability of 0.72 and a Sensitivity of 0.45 on the test set, corresponding to an F-measure of 0.48 and an Accuracy of 0.908.

Language: English

Type (Professor's evaluation): Scientific