Good News or Bad News? Conducting Sentiment Analysis on Dutch Text to Distinguish Between Positive and Negative Relations

Bibliographic Data

ID12972208
AuthorsWouter Van Atteveldt (0000-0003-1237-538X, Vrije Universiteit Amsterdam, corresponding author), Jan Kleinnijenhuis (0000-0001-6231-8186, University of Amsterdam), Nel Ruigrok (0009-0000-0552-9673, Amsterdam University College), Stefan Schlobach (0000-0002-3282-1597, Vrije Universiteit Amsterdam)
Year2008
Volume5
Issue1
Pages73-94
Publication date2008-07-14
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Information Technology & Politics (JOURNAL)
Journal identifiersISSN: 1933-169X • E-ISSN: 1933-1681
PublisherRoutledge (PUBLISHER • GB)
DOI10.1080/19331680802154145
OpenAlexW2014332385
LanguageEN
Citations received22
References cited46

Many research questions in political communication can be answered by representing text as a network of positive or negative relations between actors and issues such as conducted by semantic network analysis. This article presents a system for automatically determining the polarity (positivity/negativity) of these relations by using techniques from sentiment analysis. We used a machine learning model trained on the manually annotated news coverage of the Dutch 2006 elections, collecting lexical, syntactic, and word-similarity based features, and using the syntactic analysis to focus on the relevant part of the sentence. The performance of the full system is significantly better than the baseline with an F1 score of .63. Additionally, we replicate four studies from an earlier analysis of these elections, attaining correlations of greater than .8 in three out of four cases. This shows that the presented system can be immediately used for a number of analyses

Baseline (sea · Cognitive psychology · Focus (optics · Linguistics · Natural language processing · Negation · Negativity effect · Polarity (international relations · Political science · Sentence · Sentiment analysis · Similarity (geometry · Word (group theory · Computational and Text Analysis Methods · Computer Science · Electoral Systems and Political Participation · Psychology · Social Media and Politics · Artificial Intelligence

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