TY - CHAP
T1 - Practical Text Analytics
T2 - Maximizing the Value of Text Data
AU - Anandarajan, Murugan
AU - Hill, Chelsey
AU - Nolan, Thomas
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.
AB - This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.
KW - Automated Content Analysis
KW - Classification models
KW - Content analysis perspectives
KW - Corpus Generation
KW - Parsing
KW - Sentiment tracking
KW - Singular Value Decomposition
KW - Tag clouds
KW - Text analytics algorithms
KW - Text analytics methodology
KW - Text analytics software
KW - Text classification
KW - Text mining
KW - Text parsing
KW - Text visualization
KW - Theme Extraction
KW - Topic extraction
KW - Unstructured Data Analysis
UR - https://www.scopus.com/pages/publications/105028996780
U2 - 10.1007/978-3-319-95663-3
DO - 10.1007/978-3-319-95663-3
M3 - Chapter
AN - SCOPUS:105028996780
T3 - Advances in Analytics and Data Science
SP - 1
EP - 282
BT - Advances in Analytics and Data Science
PB - Springer Nature
ER -