Deep learning-based sentiment classification: a comparative survey
FECHA:
2020-05
IDENTIFICADOR UNIVERSAL: http://hdl.handle.net/11093/3650
VERSIÓN EDITADA: https://ieeexplore.ieee.org/document/9085334/
TIPO DE DOCUMENTO: article
RESUMEN
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.