Application of Machine Learning in Text Mining
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 35388
Special Issue Editors
Interests: natural language processing; sentiment analysis; information retrieval; information extraction; machine translation
Interests: data mining; machine learning; time series analysis; anomaly detection; process mining
Special Issue Information
Dear Colleagues,
According to Wikipedia, “Text mining, also referred to as text data mining, similar to text analytics, is the process of deriving high-quality information from text.” The definition of text mining is an automatic process of extracting knowledge from unstructured text—especially Web text, books, emails, reviews or micro-text of SNS, clinical medical records, lyrics, etc.---using natural language techniques. The process has many subtasks, such as text classification, text clustering, text summarization, text visualization, information retrieval, information extraction, word and/or document embeddings, and so on. Text mining can be broadly applied in areas such as economics, education, academic research, government, marketing, and business. It has a wide range of applications in patent analysis, copyright analysis, internet security, text classification for news articles, bioinformatics, anti-spam filtering, lyric text mining, advertisement funnel, sentiment analysis (product reviews, customer surveys, movie reviews, polls, etc.), and more. The topics of interest for this Special Issue include but are not limited to the following:
- Information retrieval;
- Information extraction;
- Relation extraction;
- Named-entity recognition;
- Sentiment analysis;
- Text categorization;
- Text clustering;
- Text summarization;
- Fake news detection;
- Topic detection;
- Trend detection;
- Topic tracking;
- Language detection;
- Intent detection;
- Keyword extraction.
We invite the submission of both original research and review articles. Additionally, invited papers based on excellent contributions to recent conferences in this field will be included in this Special Issue. We hope that this collection of high-quality works in text mining will serve as an inspiration for future research in the field.
Prof. Dr. Jae-Hoon Kim
Prof. Dr. Kichun Lee
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. Applied Sciences 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.
Keywords
- information retrieval
- information extraction
- relation extraction
- named-entity recognition
- sentiment analysis
- text categorization
- text clustering
- text summarization
- fake news detection
- topic detection
- trend detection
- topic tracking
- language detection
- intent detection
- keyword extraction