Deep Learning for Sentiment Analysis: Latest Advances and New Challenges
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 46
Special Issue Editor
Interests: sentiment analysis; social media analytics; data science; spatiotemporal databases
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Sentiment analysis has become one of the most dynamic research areas in natural language processing, extending its impact beyond computing to disciplines such as management, social sciences, finance, political science, communications, and public health. Its growing relevance to businesses and society has fueled this expansion.
While earlier studies in sentiment analysis introduced a variety of supervised and unsupervised techniques, deep learning has recently emerged as a dominant approach, offering state-of-the-art performance. Over the past decade, deep learning has proven especially effective for sentiment analysis by enabling automatic learning of complex linguistic patterns, an area where traditional machine learning methods often fall short. It consistently outperforms classical approaches in benchmark tasks, particularly in handling nuanced expressions such as sarcasm, idioms, and mixed sentiments. Pre-trained models like BERT, RoBERTa, and GPT have further advanced the field by allowing fine-tuning on sentiment datasets with limited labelled data, significantly enhancing performance. Additionally, deep learning models are highly adaptable across languages and domains, requiring minimal reconfiguration compared to rule-based and conventional machine learning techniques.
This Special Issue will focus on any advances in deep learning research for sentiment analysis. Indicative topics are provided below—please note that submissions are not limited to these areas:
- Deep learning for aspect-based sentiment analysis;
- Deep learning for emotion detection;
- Weak-supervised deep learning for sentiment analysis;
- Bias detection within deep learning for sentiment analysis;
- Deep learning for toxicity and hate speech detection;
- Multilingual deep learning for sentiment analysis;
- Social media-based sentiment analysis;
- Word embeddings for sentiment analysis;
- Irony and spamicity detection;
- Uncertainty in sentiment analyzers;
- Sentiment analysis system models for detecting malicious information propagation.
Dr. Marco Palomino
Guest Editor
Manuscript Submission Information
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Keywords
- deep learning
- aspect-based sentiment analysis
- multilingual deep learning
- word embeddings
- natural language processing
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