Optimising Twitter-based Political Election Prediction with Relevance andSentiment Filters

Eric Sanders, Antal van den Bosch

Abstract
We study the relation between the number of mentions of political parties in the last weeks before the elections and the election results. In this paper we focus on the Dutch elections of the parliament in 2012 and for the provinces (and the senate) in 2011 and 2015. With raw counts, without adaptations, we achieve a mean absolute error (MAE) of 2.71% for 2011, 2.02% for 2012 and 2.89% for 2015. A set of over 17,000 tweets containing political party names were annotated by at least three annotators per tweet on ten features denoting communicative intent (including the presence of sarcasm, the message’s polarity, the presence of an explicit voting endorsement or explicit voting advice, etc.). The annotations were used to create oracle (gold-standard) filters. Tweets with or without a certain majority annotation are held out from the tweet counts, with the goal of attaining lower MAEs. With a grid search we tested all combinations of filters and their responding MAE to find the best filter ensemble. It appeared that the filters show markedly different behaviour for the three elections and only a small MAE improvement is possible when optimizing on all three elections. Larger improvements for one election are possible, but result in deterioration of the MAE for the other elections.
Anthology ID:
2020.lrec-1.756
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6158–6165
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.756
DOI:
Bibkey:
Cite (ACL):
Eric Sanders and Antal van den Bosch. 2020. Optimising Twitter-based Political Election Prediction with Relevance andSentiment Filters. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6158–6165, Marseille, France. European Language Resources Association.
Cite (Informal):
Optimising Twitter-based Political Election Prediction with Relevance andSentiment Filters (Sanders & van den Bosch, LREC 2020)
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