The document discusses the evolution of sentiment analysis technology and its advantages over traditional qualitative coding methods. It provides examples of how sentiment analysis can be used to analyze large amounts of text data from customer reviews to understand customer sentiment and identify outliers. The document also presents a case study where sentiment analysis was used to analyze over 5 million hotel guest reviews to provide insights for hotel brand management.
4. AA Text Mining vs Qualitative Qualitative Identified Concepts Text Mining Identified Concepts Universe of text data in a study Extreme Outliers Qualitative analysis only accounts for a small sample of the available data set. Concept proportionality, importance and relevance can get distorted. Extreme outliers might be overlooked. Text mining accounts for most of the data. Extraction of concepts and categorization of data are more accurate. Extreme outliners can be identified.
5. Validation Through Triangulation Data Mining/Visualization Neural Nets, Factoring, Clustering, Logistic Regression… I. Quantitative Triangulated Validation III. Qualitative II. Psychological Text Mining (non a priori) Random Sample (a priori) Review/Confirmation Psychological Measures Review/Confirmation Verbatim Concepts and Themes
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7. Listening to the “The Voice of a Million Customers” “… Check-In” “ Good…” “ Not Clean…” “… Not Working” “ Disappointed…” “ Excellent…” “ Loud…” “… Not Friendly” “… Management” “… Charge” *For Example Only/Concepts Disguised
17. *Variables NOT used in clustering LinkedIn Segments – Important Variables (Neural Net) LI Interests/Purpose Work Situation* Purchase Behavior* Use of LinkedIn