Deep Learning-Based Sentiment Classification: A Comparative Survey

• Published in: IEEE access Vol. 8; pp. 85616 - 85638
• Main Authors: Mabrouk, Alhassan, Redondo, Rebeca P. Diaz, Kayed, Mohammed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Data mining; Datasets; Deep learning; Machine learning; neural networks; Restaurants; Review mining; Sentiment analysis; sentiment classification
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2992013

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.