| Abstract |
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 paper
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 0.63. Additionally, we replicate four studies from an earlier analysis of
these elections, attaining correlations of >0.8 in three out of four cases.
This shows that the presented system can be immediately used for a number of
analyses.
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