Building Type Classification from Social Media Texts via Geo-Spatial Textmining

Matthias Haberle, Martin Werner, Xiao Xiang Zhu

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

12 Zitate (Scopus)

Abstract

In this work, we present a model for building type classification from Twitter text messages (tweets) by employing geo-spatial textmining methods. First, we apply standard text pre-processing methods and convert the tweets into sentence vectors using fastText. For classification, we apply a feedforward network with two fully connected hidden layers and feed the generated sentence vectors as linguistic features. Classification results suggest that the classes are distinguishable to a certain extent with pure text even with unbalanced class distributions and a very small sample size. However, these findings also undermine, that building type classification with pure text data is a challenging task.

OriginalspracheEnglisch
Titel2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten10047-10050
Seitenumfang4
ISBN (elektronisch)9781538691540
DOIs
PublikationsstatusVeröffentlicht - Juli 2019
Veranstaltung39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Dauer: 28 Juli 20192 Aug. 2019

Publikationsreihe

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Konferenz

Konferenz39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Land/GebietJapan
OrtYokohama
Zeitraum28/07/192/08/19

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