TY - CHAP

T1 - Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics

AU - Lommel, Lasse

AU - Riebeling, Meike

AU - Funk, Burkhardt

AU - Junginger, Christian

N1 - Conference code: 14

PY - 2019

Y1 - 2019

N2 - Traditional unsupervised topic modeling approaches like Latent Dirichlet Allocation (LDA) lack the ability to classify documents into a predefined set of topics. On the other hand, supervised methods require significant amounts of labeled data to perform well on such tasks. We develop a new unsupervised method based on word embeddings to classify documents into predefined topics. We evaluate the predictive performance of this novel approach and compare it to seeded LDA. We use a real-world dataset from online advertising, which is comprised of markedly short documents. Our results indicate the two methods may complement one another well, leading to remarkable sensitivity and precision scores of ensemble learners trained thereupon.

AB - Traditional unsupervised topic modeling approaches like Latent Dirichlet Allocation (LDA) lack the ability to classify documents into a predefined set of topics. On the other hand, supervised methods require significant amounts of labeled data to perform well on such tasks. We develop a new unsupervised method based on word embeddings to classify documents into predefined topics. We evaluate the predictive performance of this novel approach and compare it to seeded LDA. We use a real-world dataset from online advertising, which is comprised of markedly short documents. Our results indicate the two methods may complement one another well, leading to remarkable sensitivity and precision scores of ensemble learners trained thereupon.

KW - Business informatics

KW - topic modeling, word embeddings, LDA, seeded LDA

KW - topic modeling

KW - word embeddings

KW - LDA

KW - seeded LDA

UR - https://wi2019.de/tagungsband/

UR - https://wi2019.de/wp-content/uploads/Tagungsband_WI2019_reduziert.pdf

UR - https://www.universi.uni-siegen.de/katalog/einzelpublikationen/897618.html

U2 - 10.25819/ubsi/1016

DO - 10.25819/ubsi/1016

M3 - Article in conference proceedings

SP - 453

EP - 467

BT - Human Practice. Digital Ecologies. Our Future

A2 - Ludwig, Thomas

A2 - Pipek, Volkmar

PB - Universitätsverlag Siegen

CY - Siegen

T2 - 14. Internationale Tagung Wirtschaftsinformatik - WI 2019

Y2 - 24 February 2019 through 27 February 2019

ER -