Publicación

Autores
Calvo Castro Francisco Hiram
Juárez Gambino Joel Omar
Título Cascading Classifiers for Twitter Sentiment Analysis with Emotion Lexicons
Tipo Revista
Sub-tipo Indefinido
Descripción CICLING 2016, LNCS
Resumen Many different attempts have been made to determine sentiment polarity in tweets, using emotion lexicons and different NLP techniques with machine learning. In this paper we focus on using emotion lexicons and machine learning only, avoiding the use of additional NLP techniques. We present a scheme that is able to outperform other systems that use both natural language processing and distributional semantics. Our proposal consists on using a cascading classifier on lexicon features to improve accuracy. We evaluate our results with the TASS 2015 corpus, reaching an accuracy only 0.07 below the top-ranked system for task 1, 3 levels, whole test corpus. The cascading method we implemented consisted on using the results of a first stage classification with Multinomial Naïve Bayes as additional columns for a second stage classification using a Naïve Bayes Tree classifier with feature selection. We tested with at least 30 different classifiers and this combination yielded the best results
Observaciones https://link.springer.com/chapter/10.1007%2F978-3-319-75487-1_21 http://doi.org/10.1007/978-3-319-75487-1_21
Lugar
País Mexico
No. de páginas 270-280
Vol. / Cap. 9624
Inicio 2018-03-21
Fin
ISBN/ISSN 0302-9743