Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Author Profiling Using Semantic and Syntactic Features: Notebook for PAN at CLEF 2019
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary. (EISLAB Machine Learning)ORCID iD: 0000-0002-0546-116X
Institute of Informatics, University of Szeged, Szeged, Hungary.
MindGarage, Kaiserslautern, Germany.
MindGarage, Kaiserslautern, Germany.
Show others and affiliations
2019 (English)In: CLEF 2019 Working Notes: Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum / [ed] Linda Cappellato, Nicola Ferro, David E. Losada, Henning Müller, 2019Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present an approach for the PAN 2019 Author Profiling challenge. The task here is to detect Twitter bots and also to classify the gender of human Twitter users as male or female, based on a hundred select tweets from their profile. Focusing on feature engineering, we explore the semantic categories present in tweets. We combine these semantic features with part of speech tags and other stylistic features – e.g. character floodings and the use of capital letters – for our eventual feature set. We have experimented with different machine learning techniques, including ensemble techniques, and found AdaBoost to be the most successful (attaining an F1-score of 0.99 on the development set). Using this technique, we achieved an accuracy score of 89.17% for English language tweets in the bot detection subtask

Place, publisher, year, edition, pages
2019.
National Category
Language Technology (Computational Linguistics)
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-76936OAI: oai:DiVA.org:ltu-76936DiVA, id: diva2:1373972
Conference
CLEF 2019
Available from: 2019-11-28 Created: 2019-11-28 Last updated: 2019-11-28

Open Access in DiVA

No full text in DiVA

Authority records BETA

Liwicki, Marcus

Search in DiVA

By author/editor
Kovács, GyörgyLiwicki, Marcus
By organisation
Embedded Internet Systems Lab
Language Technology (Computational Linguistics)

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 44 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf