Applications of Natural Language Processing to Data Science

Dear Colleagues,

This Special Issue aims to explore advanced techniques and emerging applications of Natural Language Processing (NLP) within the context of data science. In recent years, the field of NLP has made significant strides due to advancements in machine learning, deep learning, and the availability of large datasets. This evolution has opened new avenues for research and practical implementation across various domains, including business, healthcare, finance, education, and many others.

The main objectives of this Special Issue are as follows:

• To explore cutting-edge techniques in NLP and data science.
• To present new methodologies and tools for natural language processing.
• To discuss current challenges and possible solutions in the application of NLP.
• To examine case studies and real-world applications of NLP.
• To promote interdisciplinary research and innovation in the field.

Topics of Interest:

This Special Issue welcomes original, high-quality contributions that address, but are not limited to, the following topics:

• Advanced Language Models: Transformers and deep learning models for NLP, BERT, GPT, and their variants.
• Multilingual Natural Language Processing: Challenges and solutions for handling multilingual data.
• Sentiment Analysis and Opinion Mining: Techniques and applications for extracting and analyzing online opinions.
• Conversational Agents: Methods for automatic text generation and the development of intelligent chatbots.
• Information Extraction: Techniques for automatic extraction of structured information from unstructured texts.
• Applications of NLP in healthcare.
• Applications of NLP in business and finance.
• Applications of NLP in green economy.
• Integration of NLP and Big Data: Methods for processing large volumes of textual data, scalability, and performance.

Prof. Dr. Vincenza Carchiolo
Dr. Michele Malgeri
Guest Editors

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