A Review of Deep Learning Models for Twitter Sentiment Analysis
Challenges and Opportunities
Bibliographic Data
| ID | 22106939 |
|---|---|
| Authors | Latika 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) |
| Year | 2024 |
| Volume | 11 |
| Issue | 3 |
| Pages | 3550-3579 |
| Publication date | 2024-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3322002 |
| OpenAlex | W4388190933 |
| Language | EN |
| Citations received | 2 |
| References cited | 135 |
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
Enriching Word Vectors with Subword Information
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
Deep Contextualized Word Representations
Sentiment analysis algorithms and applications
A survey on sentiment analysis methods, applications, and challenges
Vader
Bidirectional recurrent neural networks
Long Short-Term Memory
Covid-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques
Detecting psychological change through mobilizing interactions and changes in extremist linguistic style
CitEnergy
A Coefficient of Agreement for Nominal Scales
Can social media reveal the preferences of voters? A comparison between sentiment analysis and traditional opinion polls
More than Bags of Words
Neural Network Methods for Natural Language Processing
Lexicon-Based Methods for Sentiment Analysis
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |