A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis (SA) entails extracting and identifying user opinions from the text, and various lexicon-and machine learning-based methods have been developed over the years to accomplish this. However, deep learning (DL)-based approaches have recently become dominant due to their superior performance. This study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of DL-based approaches. In addition, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures and the resources available in the public domain to aid SA tasks. Furthermore, the survey discusses domain-specific practical applications of SA tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation.

Automated summary

This study introduces the importance of sentiment analysis on the microblogging site Twitter. It provides an overview of standard preprocessing techniques and word embeddings for data preparation, and offers a comprehensive summary of deep learning-based approaches. Additionally, the study compiles popular benchmark datasets and discusses domain-specific practical applications of sentiment analysis tasks. Finally, it concludes with research challenges and outlines future prospects for further investigation.