How to evaluate sentiment classifiers for Twitter time-ordered data?

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Mozetic, I (Autor) Outra; Torgo, L (Autor) FCUP; Cerqueira, V (Autor) Outra; Smailovic, J (Autor) Outra

Revista

Título: PLoS ONE

Vol. 13 Nº 1

ISSN: 1932-6203

Editora: Public Library of Science

Indexação

ISI Web of Knowledge - 19 Citações

Scopus - 26 Citações

Outras Informações

ID Authenticus: P-00N-R0E

DOI: 10.1371/journal.pone.0194317

Abstract (EN): Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on sentiment classification of Twitter data. Construction of sentiment classifiers is a standard text mining task, but here we address the question of how to properly evaluate them as there is no settled way to do so. Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data. The problem we address concerns the procedures used to obtain reliable estimates of performance measures, and whether the temporal ordering of the training and test data matters. We collected a large set of 1.5 million tweets in 13 European languages. We created 138 sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations. The corresponding 138 in-sample data-sets are used to empirically compare six different estimation procedures: three variants of cross-validation, and three variants of sequential validation (where test set always follows the training set). We find no significant difference between the best cross-validation and sequential validation. However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it. Standard cross-validation with random selection of examples is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 20