Content and style features for automatic detection of users’ intentions in tweets

Helena Gómez-Adorno, David Pinto, Manuel Montes, Grigori Sidorov, Rodrigo Alfaro

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

6 Citas (Scopus)

Resumen

The aim of this paper is to evaluate the use of content and style features in automatic classification of intentions of Tweets. For this we propose different style features and evaluate them using a machine learning approach. We found that although the style features by themselves are useful for the identification of the intentions of tweets, it is better to combine such features with the content ones. We present a set of experiments, where we achieved a 9.46 % of improvement on the overall performance of the classification with the combination of content and style features as compared with the content features.

Idioma originalInglés
Páginas (desde-hasta)120-128
Número de páginas9
PublicaciónLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8864
DOI
EstadoPublicada - 2014

Huella

Profundice en los temas de investigación de 'Content and style features for automatic detection of users’ intentions in tweets'. En conjunto forman una huella única.

Citar esto