Sentiment Analysis Based on Deep Learning: A Comparative Study

By Nhan Cach Dang, María N. Moreno García, Fernando De la Prieta Pintado. 2020

618 citations recorded by the source corpus.

The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users’ opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Su Lin Blodgett
  2. Alexandra Sasha Luccioni
  3. Irene Solaiman
  4. Zeerak Talat
  5. Dylan Baker
  6. Margaret Mitchell
  7. Yacine Jernite
  8. Noah A. Smith

In Information Systems

  1. Irene Solaiman
  2. Marie-Therese Png
  3. Zeerak Talat
  4. Yacine Jernite
  5. Alexandra Sasha Luccioni
  6. Jesse Dodge
  7. Su Lin Blodgett
  8. Margaret Mitchell

In Natural Language Processing

  1. Alexandra Sasha Luccioni
  2. Dylan Baker
  3. Margaret Mitchell
  4. Zeerak Talat
  5. Su Lin Blodgett
  6. Irene Solaiman
  7. Yacine Jernite
  8. William Agnew

Citations in the corpus