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Deep Learning-Based Sentiment Classification: A Comparative Survey
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DOI：10.1109/ACCESS.2020.2992013.png)
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摘要
En 中文
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.
Keyword:
Review mining
sentiment classification
neural networks
deep learning
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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