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Making reading an active, observable process can be very beneficial to struggling readers. A good reader interacts with the text in order to develop an understanding of the information before them. Some good reader strategies are predicting, connecting, inferring, summarizing, analyzing and critiquing.
Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." [1] Written resources may include websites, books, emails, reviews, and ...
Text analysis is the discovery and understanding of valuable information in unstructured or large data. [47] It is a method to transform raw data into business information, allowing for strategic business decisions by offering a method to extract and examine data, derive patterns and finally interpret the data.
Semantic analytics, also termed semantic relatedness, is the use of ontologies to analyze content in web resources. This field of research combines text analytics and Semantic Web technologies like RDF. Semantic analytics measures the relatedness of different ontological concepts.
TAPoR (Text Analysis Portal for Research) is a gateway that highlights tools and code snippets usable for textual criticism of all types. The project is housed at the University of Alberta, and is currently led by Geoffrey Rockwell, Stéfan Sinclair, Kirsten C. Uszkalo, and Milena Radzikowska.
Online content analysis or online textual analysis refers to a collection of research techniques used to describe and make inferences about online material through systematic coding and interpretation. Online content analysis is a form of content analysis for analysis of Internet-based communication.
The simplest and most objective form of content analysis considers unambiguous characteristics of the text such as word frequencies, the page area taken by a newspaper column, or the duration of a radio or television program. Analysis of simple word frequencies is limited because the meaning of a word depends on surrounding text.
Topic analysis consists of two main tasks: topic identification and text segmentation. While the first is a simple classification of a specific text, the latter case implies that a document may contain multiple topics, and the task of computerized text segmentation may be to discover these topics automatically and segment the text accordingly ...
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