# Sentiment Analysis Based on Deep Learning: A Comparative Study

arXiv: https://arxiv.org/abs/2006.03541 · HTML: https://mlsanity.com/paper/2006.03541/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 30 of 37)

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- [Sentiment Analysis of Political Tweets for Israel using Machine Learning](https://mlsanity.com/paper/2204.06515/) (2022)
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## Abstract (arXiv)

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