Special Issue "Text Mining: Classification, Clustering, and Summarization"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (15 April 2019)

Special Issue Editor

Guest Editor
Prof. Dr. Duke Taeho Jo

School of Game, Hongik University, Seoul, Korea
Website | E-Mail
Interests: text mining; neural networks; machine learning; information retrieval

Special Issue Information

Dear Colleagues,

Text mining is defined as the process of extract implicit knowledge from textual data, as a special type of data mining. Main instances of text mining are text classification, text clustering, text summarization, and text segmentation. The text classification means the process of classifying a text into one among predefined categories; especially, spam mail filtering is the typical instance of text categorization. Text clustering means the process of segmenting a group of texts into subgroups each of which contains content based similar texts. Recently, as well as machine learning algorithms, such as K Nearest Neighbour, Naïve Bayes, and Support Vector Machine, deep learning algorithms are applied to the text classification.

The Special Issue on “Text Mining: Classification, Clustering, and Summarization” aims to improve the performances of each text mining tasks by applying deep learning algorithms, to derive hybrid tasks by combing the text mining tasks with each other, and to apply the text mining tasks to the real problems such as fraud document detection and financial prediction. Authors should submit papers describing significant, original and unpublished work. Possible topics include, but are not limited to:

  • Machine Learning Algorithms to improve Text Mining Tasks
  • Application of Deep Learning Algorithms to Text Mining Tasks
  • Application of Text Mining System to Real Tasks
  • Hybrid Text Mining Tasks
  • Web Mining: Web Contents Mining, Web Structure Mining, and Web Usage Mining
  • Multimedia Mining: Hybrid Mining of Multimedia Data

Prof. Dr. Duke Taeho Jo
Guest Editor

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 papers will be 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. Information is an international peer-reviewed open access monthly 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 1000 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.


  • Word Classification
  • Word Clustering
  • Automatic Keyword Extraction
  • Index Optimization
  • Text Classification
  • Text Clustering
  • Text Summarization
  • Text Segmentation
  • Machine Learning
  • Deep Learning

Published Papers

There is no accepted submissions to this special issue at this moment.
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