Volltext-Downloads (blau) und Frontdoor-Views (grau)

TWEETDICT : Identification of Topically Related Twitter Hashtags

  • This paper presents the TWEETDICT system prototype, which uses co-occurrence and frequency distributions of Twitter hashtags to generate clusters of keywords that could be used for topic summarization/identification. They also contain mentions referring to the same entity, which is a valuable resource for coreference resolution. We provide a web interface to the co-occurrence counts where an interactive search through the dataset collected from Twitter can be started. Additionally, the used data is also made freely available.

Download full text files

Export metadata

Additional Services

Share in Twitter    Search Google Scholar    frontdoor_oas
Author:Fabian Dreer, Eduard Saller, Patrick Elsässer, Desislava Zhekova
Parent Title (English):Workshop proceedings of the 12th edition of the KONVENS conference
Document Type:Conference Proceeding
Date of Publication (online):2014/11/25
Release Date:2014/11/25
Tag:IBK; Internet-basierte Kommunikation; Soziale Medien
GND Keyword:Computerunterstützte Kommunikation; Onlinecommunity
First Page:53
Last Page:57
PPN:Link zum Katalog
Institutes:Fachbereich III / Informationswissenschaft und Sprachtechnologie
DDC classes:400 Sprache / 400 Sprache, Linguistik
Collections:KONVENS 2014 / Workshop Proceedings of the 12th KONVENS 2014
Licence (German):License LogoCreative Commons - Namensnennung 3.0