Difference between revisions of "Text Mining"
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| − | ''' | + | == What is Text Mining? == |
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| + | '''Text mining''', also known as text data mining or knowledge discovery from text, is the process of extracting useful and relevant information from text data. Text mining involves using techniques from natural language processing, machine learning, and data mining to analyze and interpret large amounts of unstructured text data. | ||
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| + | Text mining can be used to uncover patterns, trends, and relationships in text data that might not be immediately apparent. It can also be used to extract structured data from text, such as names, dates, and numerical values, and to classify text into predefined categories. | ||
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| + | Text mining has a wide range of applications, including sentiment analysis, content analysis, topic modeling, and information retrieval. It is used in a variety of fields, including marketing, finance, healthcare, and social media. | ||
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| + | Examples of text mining include: | ||
| + | #Analyzing customer reviews to identify common themes and sentiments | ||
| + | #Extracting structured data from resumes to populate job applicant databases | ||
| + | #Identifying trends and patterns in social media posts | ||
| + | #Analyzing news articles to identify key themes and events | ||
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| + | ==See Also== | ||
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| + | ==References== | ||
| + | <references /> | ||
Revision as of 15:59, 3 January 2023
What is Text Mining?
Text mining, also known as text data mining or knowledge discovery from text, is the process of extracting useful and relevant information from text data. Text mining involves using techniques from natural language processing, machine learning, and data mining to analyze and interpret large amounts of unstructured text data.
Text mining can be used to uncover patterns, trends, and relationships in text data that might not be immediately apparent. It can also be used to extract structured data from text, such as names, dates, and numerical values, and to classify text into predefined categories.
Text mining has a wide range of applications, including sentiment analysis, content analysis, topic modeling, and information retrieval. It is used in a variety of fields, including marketing, finance, healthcare, and social media.
Examples of text mining include:
- Analyzing customer reviews to identify common themes and sentiments
- Extracting structured data from resumes to populate job applicant databases
- Identifying trends and patterns in social media posts
- Analyzing news articles to identify key themes and events