@inproceedings{robertson-leone-2024-dataset,
    title = "A Dataset for Multi-Scale Film Rating Inference from Reviews",
    author = "Robertson, Frankie  and
      Leone, Stefano",
    editor = "Abercrombie, Gavin  and
      Basile, Valerio  and
      Bernadi, Davide  and
      Dudy, Shiran  and
      Frenda, Simona  and
      Havens, Lucy  and
      Tonelli, Sara",
    booktitle = "Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP (NLPerspectives) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.nlperspectives-1.16",
    pages = "142--150",
    abstract = "This resource paper introduces a dataset for multi-scale rating inference of film review scores based upon review summaries. The dataset and task are unique in pairing a text regression problem with ratings given on multiple scales, e.g. the A-F letter scale and the 4-point star scale. It retains entity identifiers such as film and reviewer names. The paper describes the construction of the dataset before exploring potential baseline architectures for the task, and evaluating their performance. Baselines based on classifier-per-scale, affine-per-scale, and ordinal regression models are presented and evaluated with the BERT-base backbone. Additional experiments are used to ground a discussion of the different architectures{'} merits and drawbacks with regards to explainability and model interpretation.",
}

This resource paper introduces a dataset for multi-scale rating inference of film review scores based upon review summaries. The dataset and task are unique in pairing a text regression problem with ratings given on multiple scales, e.g. the A-F letter scale and the 4-point star scale. It retains entity identifiers such as film and reviewer names. The paper describes the construction of the dataset before exploring potential baseline architectures for the task, and evaluating their performance. Baselines based on classifier-per-scale, affine-per-scale, and ordinal regression models are presented and evaluated with the BERT-base backbone. Additional experiments are used to ground a discussion of the different architectures{'} merits and drawbacks with regards to explainability and model interpretation.
Robertson, Frankie Leone, Stefano
A Dataset for Multi-Scale Film Rating Inference from Reviews