VAD_KnessetCorpus

This dataset extends the original Knesset Corpus by adding VAD (Valence, Arousal, Dominance) annotations to committee sentences.

The VAD scores were generated using the VAD binomial regression models, which were developed specifically for VAD prediction, on embeddings produced by the Knesset-multi-e5-large.

Additionally, a small subset of 120 Knesset sentences has been manually annotated by three annotators for VAD scores, and the file is available in the repository.

ArXiv The Knesset Corpus ArXiv Unveiling Affective Polarization Trends in Parliamentary Proceedings

VAD_scores:

• Valence (or pleasure) measures the positivity or negativity of an emotion on a scale from 0 to 1.

• Arousal quantifies the intensity of an emotion, from calm to excited on a scale from 0 to 1.

• Dominance represents the degree of control conveyed by a word, from submissive to dominant on a scale from 0 to 1.

Usage

After dowloading and extracting jsonl file from here: https://huggingface.co/datasets/GiliGold/VAD_KnessetCorpus/tree/main

vad_shard_path = "women_rights_vad_shards_bzip2_files\vad_shard_0.jsonl" #adjust path accordingly

with open(vad_shard_path, encoding="utf-8") as vad_file:
  for line in vad_file:
      sentence_entity = json.loads(line)
      sentence_text = sentence_entity["sentence_text"]
      v = sentence_entity["vad_values"]["valence"]
      a = sentence_entity["vad_values"]["arousal"]
      d = sentence_entity["vad_values"]["dominance"]
      print(f'sentence text: {sentence_text}')
      print(f'valence value is: {v}')

Cite

@article{10.1162/COLI.a.600,
    author = {Goldin, Gili and Rabinovich, Ella and Wintner, Shuly},
    title = {Unveiling Affective Polarization Trends in Parliamentary Proceedings},
    journal = {Computational Linguistics},
    pages = {1-33},
    year = {2026},
    month = {01},
    abstract = {Recent years have seen an increase in polarized discourse worldwide, on various platforms. We propose a novel method for quantifying polarization, based on the emotional style of the discourse rather than on differences in ideological stands. Using measures of Valence, Arousal and Dominance, we detect signals of emotional discourse and use them to operationalize the concept of affective polarization. Applying this method to a recently released corpus of proceedings of the Knesset, the Israeli parliament (in Hebrew), we find that the emotional style of members of government differs from that of opposition members; and that the level of affective polarization, as reflected by this style, is significantly increasing with time.},
    issn = {0891-2017},
    doi = {10.1162/COLI.a.600},
    url = {https://doi.org/10.1162/COLI.a.600},
    eprint = {https://direct.mit.edu/coli/article-pdf/doi/10.1162/COLI.a.600/2573634/coli.a.600.pdf},
}