  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.
