Challenge: Transcripts of UK parliamentary debates are difficult for human readers to process due to the large quantity of textual data and the specialised language used.
Approach: They propose to use annotated sentiment labels and labels derived from speakers' votes to classify the sentiment polarity of speakers as being either positive or negative towards motions proposed in the debates.
Outcome: The proposed model outperforms existing models on a dataset of parliamentary debate transcripts using textual and contextual features.

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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.
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Investigating Political Herd Mentality: A Community Sentiment Based Approach (P19-2)

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Challenge: polarities inherent in political speeches and debates pose an important problem today.
Approach: They propose to use community-based graphs to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts.
Outcome: The proposed approach surpasses the benchmark results on the same dataset.
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion (2020.coling-main)

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Challenge: Parliamentary debates are a valuable language resource for analyzing comprehensive options in a functional, free society.
Approach: They propose a neural model for political speech sentiment analysis exploiting semantic representations and relations between debate transcripts, motions, and political party members.
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Inference Annotation of a Chinese Corpus for Opinion Mining (2020.lrec-1)

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Challenge: Existing tools for opinion mining can accurately predict the writer's attitude in simple explicit sentences.
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Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)

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Challenge: Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments.
Approach: They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate.
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Classifier-based Polarity Propagation in a WordNet (L18-1)

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Challenge: a wordnet-based sentiment lexicon can be built to express sentiment polarity in a way shared across domains.
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An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews (D19-55)

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Challenge: sarcasm, humor, hate speech, and sentiment are a complex language attribute . sentiment classification models are used for complex language understanding tasks .
Approach: They propose a two-step model that extracts features pertaining to sarcasm, humour, hate speech, as well as sentiment from online reviews and feeds them to inform sentiment classification.
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Revisiting the Importance of Encoding Logic Rules in Sentiment Classification (D18-1)

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Challenge: Neural models that explicitly encode word order, syntax and semantic features are unequipped to deal with complex syntactic structures that affect sentiment, such as contrastive conjunctions.
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The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings (2024.lrec-main)

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Challenge: The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment, which is used in a series of experiments focused on training a robust sentiment identifier for parliamentary proceedings.
Approach: They propose to use a dataset of sentences manually annotated for sentiment to train a robust sentiment identifier for parliamentary proceedings.
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A Recorded Debating Dataset (L18-1)

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Challenge: Existing research in computational argumentation and debating technologies focuses on argumentation mining, but other tasks are being addressed as well.
Approach: They describe a dataset of debating speeches in English that is used for research . they use an automatic speech recognition system to produce a more "nLP-friendly" text .
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