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Improving Text Analysis Using Sentence Conjunctions and Punctuation


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Büschken, Joachim ; Allenby, Greg M.:
Improving Text Analysis Using Sentence Conjunctions and Punctuation.
Katholische Universität Eichstätt-Ingolstadt
Ingolstadt, 2017. - 53 S.


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Link zum Volltext (externe URL): https://ssrn.com/abstract=2908915


User generated content in the form of customer reviews, blogs or tweets is an emerging and rich source of data for marketers. Topic models have been successfully applied to such data, demonstrating that empirical text analysis benefits greatly from a latent variable approach which summarizes high-level interactions among words. We propose a new topic model that allows for serial dependency of topics in text. That is, topics may carry over from word to word in a document, violating the bag-of-words assumption in traditional topic models. In our model, topic carry-over is informed by sentence conjunctions and punctuation. Typically, such observed information is eliminated prior to analyzing text data (i.e., “pre-processing”) because words such as “and” and “but” do not differentiate topics. We find that these elements of grammar contain information relevant to topic changes. We examine the performance of our model using multiple data sets and estab- lish boundary conditions for when our model leads to improved inference about customer evaluations. Implications and opportunities for future research are discussed.

Weitere Angaben

Publikationsform:Preprint, Working paper, Diskussionspapier
Schlagwörter:LDA, autocorrelated topics, user-generated content, Bayesian analysis
Institutionen der Universität:Wirtschaftswissenschaftliche Fakultät > Betriebswirtschaftslehre > Lehrstuhl für Allgemeine Betriebswirtschaftslehre, Absatzwirtschaft und Marketing
Open Access: Freie Zugänglichkeit des Volltexts?:Ja
Titel an der KU entstanden:Ja
Eingestellt am:16. Aug 2017 10:44
Letzte Änderung:16. Aug 2017 10:44
URL zu dieser Anzeige:http://edoc.ku-eichstaett.de/20317/