Text-mining commonly consigns to the development of mining attractive patterns and non-trivial information from the database and gets acquaintance from non-arrangement text. Generally text mining covers several computer science restraints with a physically powerful orientation towards artificial intelligence in wide-ranging, together with but not maximum valued of given attractive patterns to r...

Text mining is becoming vital as Web 2.0 offers collaborative content creation and sharing. Now Researchers have growing interest in text mining methods for discovering knowledge. Text mining researchers come from variety of areas like: Natural Language Processing, Computational Linguistic, Machine Learning, and Statistics. A typical text mining application involves preprocessing of text, stemm...

1 Text mining draw more and more attention recently, it has been applied on different domains including web mining, opinion mining, and sentiment analysis. Text pre-processing is an important stage in text mining. The major obstacle in text mining is the very high dimensionality and the large size of text data. Natural language processing and morphological tools can be employed to reduce dimens...

This vignette gives a short introduction to text mining in R utilizing the text mining framework provided by the tm package. We present methods for data import, corpus handling, preprocessing, metadata management, and creation of term-document matrices. Our focus is on the main aspects of getting started with text mining in R—an in-depth description of the text mining infrastructure offered by ...

UNLABELLED Text mining from the biomedical literature is of increasing importance, yet it is not easy for the bioinformatics community to create and run text mining workflows due to the lack of accessibility and interoperability of the text mining resources. The U-Compare system provides a wide range of bio text mining resources in a highly interoperable workflow environment where workflows can...

Text mining is a very exciting research area as it tries to discover knowledge from unstructured texts. These texts can be found on a computer desktop, intranets and the internet. The aim of this paper is to give an overview of text mining in the contexts of its techniques, application domains and the most challenging issue. The focus is given on fundamentals methods of text mining which includ...

“Bag of words” model, acronym extraction, authorship ascription, coordinate matching, data mining, document clustering, document frequency, document retrieval, document similarity metrics, entity extraction, hidden Markov models, hubs and authorities, information extraction, information retrieval, key-phrase assignment, key-phrase extraction, knowledge engineering, language identification, link...

The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a n...

This paper provides taxonomy of common text mining tasks and approaches. We surveyed the market of modern text mining tools, compared their features and grouped them into information retrieval, standard or intelligent text mining categories in order to examine how theoretical promises materialized in modern technologies. The study is the first one in a series of studies trying to provide an und...

In this paper we describe how to mine association rules in temporal document collections. We describe how to perform the various steps in the temporal text mining process, including data cleaning, text refinement, temporal association rule mining and rule post-processing. We also describe the Temporal Text Mining Testbench, which is a user-friendly and versatile tool for performing temporal tex...