A Review of Deep Learning Models for Twitter Sentiment Analysis

Challenges and Opportunities

Datos Bibliográficos

ID22106939
AutoresLatika Chaudhary (0000-0001-7255-5804, Jaypee Institute of Information Technology), Nancy Girdhar (0000-0002-1009-3875, Laboratoire Informatique, Image et Interaction (L3i)), Deepak Sharma (0000-0001-7612-3486, Christian-Albrechts-Universität zu Kiel), Javier Andreu-Pérez (0000-0002-7421-4808, University of Essex), Antoine Doucet (0000-0001-6160-3356, Laboratoire Informatique, Image et Interaction (L3i)), Matthias Renz (0000-0002-2024-7700, Christian-Albrechts-Universität zu Kiel)
Año2024
Volumen11
Número3
Páginas3550-3579
Fecha de publicación2024-06-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaIEEE Transactions on Computational Social Systems (JOURNAL)
Identificadores de la revistaISSN: 2329-924X • E-ISSN: 2373-7476
EditorialInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2023.3322002
OpenAlexW4388190933
IdiomaEN
Citas recibidas2
Referencias citadas135

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

Data science · Deep learning · Lexicon · Microblogging · Preprocessor · Sentiment analysis · Social media · World Wide Web · Computer Science · Engineering · Sentiment Analysis and Opinion Mining · Text and Document Classification Technologies · Topic Modeling · Artificial Intelligence