Advances in Text Mining and Analytics

Dear Colleagues,

Text is the most traditional method for information recording and knowledge representation. Nowadays, textual information is growing at an astounding pace, creating an enormous challenge for analysts trying to discover valuable information that is buried. Text mining focuses on mining meaningful information from massive texts. Text analytics uses computer algorithms and techniques that enable computers to understand and interpret human language, transforming or analyzing unstructured text data into usable formats to identify patterns, trends, and insights from extensive textual data. For example, new non-trivial trends, patterns, and associations among entities of interest, such as relationships between genes, proteins, and diseases and the connections between different places or the commonalities of people, are such forms of underlying knowledge. However, great challenges face many text mining and analytic tasks because of the increasing volume of text data and the difficulty in capturing valuable knowledge hidden in them. Efficient and effective text mining and analytic techniques are demanded to tackle these existing challenges.

This Special Issue aims to present the latest research and developments in text mining and analytics, including new methods and techniques with recent advancements in machine learning, natural language processing, and artificial intelligence. Authors are invited to submit original, unpublished articles addressing the development of new text mining and analytics techniques, such as algorithms, software, and others. Applications of text mining and analytics techniques in different contexts are also welcomed, such as sentiment analysis, web mining and social network analysis, topic modeling, named entity recognition, biomedical literature mining, and healthcare informatics. Topics of interest include but are not limited to the following:

• Natural language understanding;
• Information retrieval and extraction;
• Generative AI;
• Document topic modeling;
• Knowledge discovery in text;
• Language modeling;
• Recommender systems;
• Knowledge networks and graphs;
• Named entity recognition and entity linking;
• Document semantic extraction and relation extraction;
• Sentiment analysis, opinion, and argument mining;
• Text summarization;
• Question answering systems;
• Development of software for text mining and analytics;
• Applications of text mining and analytics (such as education, healthcare, bioinformatics, finance, social media, computational life science, etc.).

Dr. Wei Jin
Dr. Yang Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• text mining
• natural language processing
• information retrieval
• text analytics
• relation extraction
• entity recognition and linking
• language models