Topic Modeling for Analyzing Open-Ended Survey Responses
Year of publication: |
2018
|
---|---|
Authors: | Pietsch, Andra-Selina ; Lessmann, Stefan |
Publisher: |
Berlin : Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series" |
Subject: | Market research | open-ended responses | text analytics | short text topic models |
Series: | IRTG 1792 Discussion Paper ; 2018-054 |
---|---|
Type of publication: | Book / Working Paper |
Type of publication (narrower categories): | Working Paper |
Language: | English |
Other identifiers: | hdl:10419/230765 [Handle] RePEc:zbw:irtgdp:2018054 [RePEc] |
Classification: | C00 - Mathematical and Quantitative Methods. General |
Source: |
-
Deep Learning application for fraud detection in financial statements
Craja, Patricia, (2020)
-
Forecasting the success of telecommunication services in the presence of network effects
Schoder, Detlef, (2000)
-
Statistisches Bundesamt Deutschland - Umweltökonomische Gesamtrechnungen (UGR)
- More ...
-
Customer-Centric Decision Support
Lessmann, Stefan, (2010)
-
A memetic approach to construct transductive discrete support vector machines
Brandner, Hubertus, (2013)
-
Lessmann, Stefan, (2012)
- More ...