Sentiment Analysis Based on Deep Learning: A Comparative Study

TL;DR

This paper reviews recent deep learning approaches for sentiment analysis on social media, comparing various models and features to evaluate their effectiveness in improving NLP sentiment classification accuracy.

Contribution

It provides a comprehensive comparison of deep learning models and input features for sentiment analysis, highlighting their relative performance across datasets.

Findings

Deep learning models outperform traditional methods in sentiment classification.

Word embeddings improve model accuracy over TF-IDF features.

Model performance varies significantly depending on input features and dataset.

Abstract

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