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. |
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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. |
‘Aye’ or ‘No’? Speech-level Sentiment Analysis of Hansard UK Parliamentary Debate Transcripts (L18-1)
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| 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. |
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. |
| Outcome: | The proposed model exploits semantic representations and relations between debate transcripts, motions, and political party members to predict political polarity and polarities. |
Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity (2022.findings-naacl)
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| Challenge: | Existing methods to detect ideological divides in social media rely on knowing in advance the political orientation of text . fascist and mainstream are among the most polarized concepts in reddit in 2019 . |
| Approach: | They propose a minimally supervised method that leverages the network structure of online discussion forums to detect polarized concepts. |
| Outcome: | The proposed framework captures temporal ideological dynamics such as right-wing and left-wing radicalization using graph neural networks and sparsity learning. |
Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts (2020.coling-main)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) aims at capturing sentiment expressed toward each aspect of a target entity. |
| Approach: | They propose to extend the task of Aspect-based Sentiment Analysis (ABSA) toward affect and emotion representation in polarized settings. |
| Outcome: | The proposed model captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m entities at sentence-level. |
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. |
| Approach: | They propose a method to build a sense-level sentiment lexicon on the basis of a wordnet . they use a rich set of wordnet-based features to recognize and assign sentiment polarity values . |
| Outcome: | The proposed method allows for the construction of a more reliable sentiment lexicon . the proposed method is partially automated, but it's performance drops in cross-domain applications . |
Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification (2025.findings-acl)
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| Challenge: | Existing methods to analyze political biases rely on small-size intermediate tasks and the LLMs themselves. |
| Approach: | They propose an entropy-based inconsistency metric to encode political biases . they insert 1319 demographically and politically diverse politician names in 450 political sentences . |
| Outcome: | The proposed method combines high accuracy with a correct understanding of the candidate candidate. |
Structured Sentiment Analysis as Dependency Graph Parsing (2021.acl-long)
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| Challenge: | Structured sentiment analysis attempts to extract full opinion tuples from a text, but has been subdivided into smaller and smaller sub-tasks, e.g., target extraction or targeted polarity classification. |
| Approach: | They propose a framework which jointly predicts all elements of an opinion tuple and their relations by using dependency graph parsing. |
| Outcome: | The proposed framework improves on five datasets in English, Norwegian, Basque, and Catalan and refining the sentiment graphs with syntactic dependency information further improves results. |
Computational Analysis of Political Texts: Bridging Research Efforts Across Communities (P19-4)
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| Challenge: | Political scientists have developed and adopted natural language processing (NLP) methods to exploit text as an additional source of data in their analyses. |
| Approach: | This tutorial aims to provide a gentle introduction to methods and tasks related to computational analysis of political texts from both communities. |
| Outcome: | The main goal of this tutorial is to bring the two research communities closer to each other and contribute to faster and more significant developments in this interdisciplinary area. |
Resources and Experiments on Sentiment Classification for Georgian (2022.lrec-1)
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| Challenge: | a dataset for sentiment classification and semantic polarity dictionary for Georgian is available . a large number of linguistic resources are available for sentiment analysis for this language . |
| Approach: | They propose to create the first publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. |
| Outcome: | The results are on par with state-of-the-art models for well-studied languages . the authors compare knowledge-and machine learning-based models to a well-supported language . |