RT Journal Article
T1 Deep learning-based sentiment classification: a comparative survey
A1 Mabrouk, Alhassan
A1 Díaz Redondo, Rebeca Pilar
A1 Kayed, Mohammed
K1 3325 Tecnología de las Telecomunicaciones
K1 3325.99 Otras
AB Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. This paper addresses these factors and classifies them into three categories: data preparation based factors, feature representation based factors and the classification techniques based factors. The paper is a comprehensive literature-based survey that compares the performance of more than 100 DL-based SC approaches by using 21 public datasets of reviews given by customers within three specific application domains (products, movies and restaurants). These 21 datasets have different characteristics (balanced/imbalanced, size, etc.) to give a global vision for our study. The comparison explains how the proposed factors quantitatively affect the performance of the studied DL-based SC approaches.
PB IEEE Access
SN 21693536
YR 2020
FD 2020-05
LK http://hdl.handle.net/11093/3650
UL http://hdl.handle.net/11093/3650
LA eng
NO IEEE Access, 8, 85616-85638 (2020)
NO Xunta de Galicia
DS Investigo
RD 14-dic-2024