Optimising Twitter-based Political Election Prediction with Relevance and Sentiment Filters

TY - CHAP

T1 - Optimising Twitter-based Political Election Prediction with Relevance and Sentiment Filters

AU - Sanders, Eric

AU - van den Bosch, A.

PY - 2020/5/15

Y1 - 2020/5/15

N2 - 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 thethree 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.

AB - 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 thethree 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.

M3 - Contribution to conference proceedings

SP - 6160‑6167

BT - Proceedings of the 12th Language Resources and Evaluation Conference

PB - European Language Resources Association (ELRA)

CY - Marseille, France

ER -