In this section, we will provide you with a brief overview
of various text mining tasks which are commonly used for analyzing large
volumes of unstructured textual data. Text classification, grouping, entity
extraction, fine-grained taxonomies, sentiment analysis, document
summarization, and entity relation modeling are some of these activities. Text
categorization involves organizing text into predefined categories based on its
content. Clustering is the process of grouping similar documents together based
on their intrinsic characteristics. Entity extraction involves identifying and
extracting key elements such as people, places, and organizations from text.
Granular taxonomies are hierarchical structures used for organizing textual
data. Determining the general sentiment of a text, whether it be favorable,
negative, or neutral, is the goal of sentiment analysis. Making a summary of a
longer material is called document summarizing. Lastly, the act of determining
the connections between various named entities that are stated in a text is
known as entity relation modeling.
Author(s) Details
Adebola K. Ojo
Department of Computer Science, University of Ibadan, Ibadan, Nigeria.
Please see the book here:- https://doi.org/10.9734/bpi/mono/978-81-972870-5-3/CH8
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