This survey reviews a broad array of techniques that are becoming available to mine textual data. It presents initially a three function (data collection, data warehousing, data exploitation) text mining architecture consisting of a six step text mining process (source selection, text retrieval, information extraction, data storage, data mining, presentation). It then presents some of the most ...

(ABSTRACT) With the prevalence of large data stored in the cloud, including unstructured information in the form of text, there is now an increased emphasis on text mining. A broad range of techniques are now used for text mining, including algorithms adapted from machine learning, NLP, computational linguistics, and data mining. Applications are also multi-fold, including classification, clust...

The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions i...

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documen...

Structuring of text document knowledge frequently appears either by ontologies and metadata or by automatic (un-)unsupervised text categorization. This paper describes our integrated framework OTTO (OnTology-based Text mining framewOrk). OTTO uses text mining to learn the target ontology from text documents and uses then the same target ontology in order to improve the effectiveness of both sup...

Automatic text classification is a popular research topic in text mining. Automatic text classification is an eminent field of research in text mining, which is tries to automatically classify the text documents into pre-specified categories. Text mining involves several pre-processing and classification techniques. In this paper, we have analysed several feature selection methods with support ...

Text is most used means for information exchange in today’s web dominated world. Text Mining also known as text data mining, an equivalent of text analytic, means the process of retrieving high quality information from text. Statistical pattern learning helps to devise patterns and trends through which high quality information is derived. Text mining accomplishes the task of structuring the inp...

Text Mining has become an important research area. Text Mining is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. In this paper, a Survey of Text Mining techniques and applications have been s presented.

This paper presents text mining using SAS® Text Miner and Megaputer PolyAnalyst® specifically applied for hotel customer survey data, and its data management. The paper reviews current literature of text mining, and discusses features of these two text mining software packages in analyzing unstructured qualitative data in the following key steps: data preparation, data analysis, and result repo...