An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets

Gregory Spell, Brian Guay, Sunshine Hillygus, Lawrence Carin


Abstract
Legislator preferences are typically represented as measures of general ideology estimated from roll call votes on legislation, potentially masking important nuances in legislators’ political attitudes. In this paper we introduce a method of measuring more specific legislator attitudes using an alternative expression of preferences: tweeting. Specifically, we present an embedding-based model for predicting the frequency and sentiment of legislator tweets. To illustrate our method, we model legislators’ attitudes towards President Donald Trump as vector embeddings that interact with embeddings for Trump himself constructed using a neural network from the text of his daily tweets. We demonstrate the predictive performance of our model on tweets authored by members of the U.S. House and Senate related to the president from November 2016 to February 2018. We further assess the quality of our learned representations for legislators by comparing to traditional measures of legislator preferences.
Anthology ID:
2020.emnlp-main.46
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
627–641
Language:
URL:
https://aclanthology.org/2020.emnlp-main.46
DOI:
10.18653/v1/2020.emnlp-main.46
Bibkey:
Cite (ACL):
Gregory Spell, Brian Guay, Sunshine Hillygus, and Lawrence Carin. 2020. An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 627–641, Online. Association for Computational Linguistics.
Cite (Informal):
An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets (Spell et al., EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.46.pdf
Video:
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