Guest Editors
Dr. G. G. Md. Nawaz Ali
Email: nali@fsmail.bradley.edu
Affiliation: Department of Computer Science and Information Systems, Bradley University, Peoria 61625, IL, USA
Homepage:
Research Interests: sentiment analysis, large language models, natural language processing, machine learning and artificial intelligence
Summary
With the explosive growth of user-generated content on platforms such as Twitter/X, Reddit, YouTube, Facebook, and TikTok, social media has emerged as a rich and dynamic source of public opinion, behavioral signals, and emotional expression. Sentiment analysis — the computational study of people's opinions, sentiments, emotions, and attitudes — has become a critical tool for extracting actionable insights from this vast stream of text data.
Historically, lexicon-based sentiment analysis approaches, which rely on curated dictionaries and sentiment scoring algorithms, have been effective for many constrained or interpretable settings. However, these methods often struggle with informal language, sarcasm, and domain adaptation. In recent years, Large Language Models (LLMs) such as BERT, RoBERTa, GPT, and LLaMA have revolutionized sentiment analysis by leveraging deep contextual embeddings and transfer learning, significantly outperforming traditional approaches on benchmark tasks.
Despite the promise of these methods, several open challenges persist. These include handling multimodal content (e.g., text with images or videos), ensuring fairness and transparency, interpreting black-box model outputs, mitigating bias, and managing evolving slang and linguistic diversity across platforms and cultures.
This special issue invites original research articles, surveys, and case studies that focus on innovations, evaluations, comparisons, and applications of sentiment analysis techniques for social media data — spanning both lexicon-based and large language model-based paradigms.
Topics of Interest (include but are not limited to):
· Lexicon-based sentiment analysis methods for social media
· Fine-tuning LLMs for domain-specific sentiment detection
· Comparative studies between lexicon and LLM-based models
· Zero-shot and few-shot sentiment classification using LLMs
· Sarcasm, irony, and emotion detection in social media posts
· Real-time sentiment monitoring and stream analysis
· Multilingual sentiment analysis and cross-lingual transfer
· Bias detection and fairness in sentiment models
· Explainable AI in sentiment analysis
· Ethical considerations and responsible use of LLMs in social contexts
· Benchmark datasets and evaluation frameworks for sentiment models
· Multimodal sentiment analysis combining text, images, and video
· Applications in politics, public health, brand reputation, and crisis detection
Keywords
Sentiment Analysis, Social Media, Lexicon-Based Models, Large Language Models, Emotion Detection, Sarcasm Analysis, Deep Learning, NLP, Fairness, Explainability, Zero-shot Learning, Multimodal Analysis
Published Papers
Login
Register
Submit a Paper
Propose a Special lssue