ParlVote: A Corpus for Sentiment Analysis of Political Debates (2020.lrec-1)

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Challenge: Debate transcripts from the UK Parliament contain information about the positions taken by politicians towards important topics, but are difficult for humans to process manually.
Approach: They propose to use a linear classifier and a transformer word embedding model to classify sentiment polarity in debate speeches to evaluate sentiment analysis systems for the political domain.
Outcome: The proposed method performs better on the largest dataset and is more robust to other datasets.

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Large Corpus of Czech Parliament Plenary Hearings (2020.lrec-1)

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