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Lec 1 NLP

The document discusses Natural Language Processing (NLP) applications, highlighting both supervised and unsupervised methods such as spam detection, sentiment analysis, and topic modeling. It emphasizes the integration of textual data into predictive modeling across various fields, including healthcare and insurance. Additionally, it provides links to various NLP tools and demonstrates the trend analysis for the term 'text mining.'

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17 views23 pages

Lec 1 NLP

The document discusses Natural Language Processing (NLP) applications, highlighting both supervised and unsupervised methods such as spam detection, sentiment analysis, and topic modeling. It emphasizes the integration of textual data into predictive modeling across various fields, including healthcare and insurance. Additionally, it provides links to various NLP tools and demonstrates the trend analysis for the term 'text mining.'

Uploaded by

abdoalsenaweabdo
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Natural Language Processing

Communication With Machines


Level Of Linguistic
Knowledge
http://text-processing.com/demo/sentiment/

https://www.csc2.ncsu.edu/faculty/healey/tweet_viz/tweet_app/

https://www.ibm.com/demos/live/natural-language-understanding/self-
service/home
IBM Watson
NLP Application
• Supervised:
• Spam Detection
• Sentiment Analysis
• Intent Classification
• Multi-Label, Multi-Class Text Classification

• Unsupervised:
• Topic Modeling
• Keyword Extraction
• Trend/Outlier detection
Text Mining Applications –Supervised
–Many typical predictive modeling or classification
applications can be enhanced by incorporating textual data in
addition to traditional input variables.
• churning propensity models that include customer center
notes, website forms, e-mails, and Twitter messages
• hospital admission prediction models incorporating
medical records notes as a new source of information
• insurance fraud modeling using adjustor notes
• sentiment categorization
• stylometry or forensic applications that identify the
author of a particular writing sample
Text Mining Applications –Unsupervised
• Text clustering • Trend analysis

Trend for the Term “text mining” from


Google Trends

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