Deep Learning-based Sentiment Classification: A Comparative Survey
It provides a comprehensive analysis for researchers and practitioners in sentiment analysis, but it is incremental as it synthesizes existing work without introducing new methods.
This paper conducts a comparative survey of over 100 deep learning-based sentiment classification approaches across 21 public datasets, analyzing how factors like data preparation and feature representation quantitatively affect performance.
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.