Comparative Study of Deep Learning-Based Sentiment Classification

Seungwan Seo, Czangyeob Kim, Haedong Kim, Kyounghyun Mo, Pilsung Kang

Research output: Contribution to journalArticlepeer-review

75 Citations (Scopus)


The purpose of sentiment classification is to determine whether a particular document has a positive or negative nuance. Sentiment classification is extensively used in many business domains to improve products or services by understanding the opinions of customers regarding these products. Deep learning achieves state-of-the-art results in various challenging domains. With the success of deep learning, many studies have proposed deep-learning-based sentiment classification models and achieved better performances compared with conventional machine learning models. However, one practical issue occurring in deep-learning-based sentiment classification is that the best model structure depends on the characteristics of the dataset on which the deep learning model is trained; moreover, it is manually determined based on the domain knowledge of an expert or selected from a grid search of possible candidates. Herein, we present a comparative study of different deep-learning-based sentiment classification model structures to derive meaningful implications for building sentiment classification models. Specifically, eight deep-learning models, three based on convolutional neural networks and five based on recurrent neural networks, with two types of input structures, i.e., word level and character level, are compared for 13 review datasets, and the classification performances are discussed under different perspectives.

Original languageEnglish
Article number8948030
Pages (from-to)6861-6875
Number of pages15
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Sentiment classification
  • character embedding
  • convolutional neural network
  • deep learning
  • recurrent neural network
  • word embedding

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science
  • General Materials Science


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