@inproceedings{sousa-pardo-2022-evaluating,
    title = "Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for {P}ortuguese",
    author = "Sousa, Rog{\'e}rio  and
      Pardo, Thiago",
    editor = "Barnes, Jeremy  and
      De Clercq, Orph{\'e}e  and
      Barriere, Valentin  and
      Tafreshi, Shabnam  and
      Alqahtani, Sawsan  and
      Sedoc, Jo{\~a}o  and
      Klinger, Roman  and
      Balahur, Alexandra",
    booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.wassa-1.19/",
    doi = "10.18653/v1/2022.wassa-1.19",
    pages = "204--213",
    abstract = "Over the years, the review helpfulness prediction task has been the subject of several works, but remains being a challenging issue in Natural Language Processing, as results vary a lot depending on the domain, on the adopted features and on the chosen classification strategy. This paper attempts to evaluate the impact of content features and classification methods for two different domains. In particular, we run our experiments for a low resource language {--} Portuguese {--}, trying to establish a benchmark for this language. We show that simple features and classical classification methods are powerful for the task of helpfulness prediction, but are largely outperformed by a convolutional neural network-based solution."
}