Text Mining Concepts (2 of 2) Benefits of text mining are obvious especially in text-rich data environments e.g., law(court orders), academic research(research articles), finance quarterly reports), medicine (discharge summaries), biology(molecular interactions), technology(patent files), marketing (customer comments), etc Electronic communization records( e.g., e-mail) Spam filtering E-mail prioritization and categorization Automatic response generation Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Text Mining Concepts (2 of 2) • Benefits of text mining are obvious especially in text-rich data environments – e.g., law (court orders), academic research (research articles), finance (quarterly reports), medicine (discharge summaries), biology (molecular interactions), technology (patent files), marketing (customer comments), etc. • Electronic communization records (e.g., e-mail) – Spam filtering – E-mail prioritization and categorization – Automatic response generation
Text Mining Application Area Information extraction ° Topic tracking Summarization Categorization Clustering Concept linking Question answering Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Text Mining Application Area • Information extraction • Topic tracking • Summarization • Categorization • Clustering • Concept linking • Question answering
Text Mining Terminology (1 of2 Unstructured or semistructured data ° Corpus( and corpora) Terms Concepts Stemming Stop words(and include words) Synonyms(and polysemes) Tokenizing Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Text Mining Terminology (1 of 2) • Unstructured or semistructured data • Corpus (and corpora) • Terms • Concepts • Stemming • Stop words (and include words) • Synonyms (and polysemes) • Tokenizing
Text Mining Terminology(2 of2 Term dictionary Word frequency Part-of-speech tagging Morphology Term-by-document matrix Occurrence matrⅸ Singular value decomposition Latent semantic indexing Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Text Mining Terminology (2 of 2) • Term dictionary • Word frequency • Part-of-speech tagging • Morphology • Term-by-document matrix – Occurrence matrix • Singular value decomposition – Latent semantic indexing
Application Case 5.1 Insurance Group Strengthens Risk Management with Text Mining Solution Questions for Discussion 1. How can text analytics and mining be used to keep up with changing business needs of insurance companies? 2. What were the challenges, the proposed solution, and the obtained results 3. Can you think of other uses of text analytics and text mining for insurance companies? Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Application Case 5.1 Insurance Group Strengthens Risk Management with Text Mining Solution Questions for Discussion 1. How can text analytics and mining be used to keep up with changing business needs of insurance companies? 2. What were the challenges, the proposed solution, and the obtained results? 3. Can you think of other uses of text analytics and text mining for insurance companies?