@inproceedings{ramos-etal-2022-movie,
    title = "Movie Rating Prediction using Sentiment Features",
    author = "Ramos, Jo{\~a}o  and
      Ap{\'o}stolo, Diogo  and
      Gon{\c{c}}alo Oliveira, Hugo",
    editor = "Kernerman, Ilan  and
      Carvalho, Sara  and
      Iglesias, Carlos A.  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the 2nd Workshop on Sentiment Analysis and Linguistic Linked Data",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.salld-1.3/",
    pages = "9--18",
    abstract = "We analyze the impact of using sentiment features in the prediction of movie review scores. The effort included the creation of a new lexicon, Expanded OntoSenticNet (EON), by merging OntoSenticNet and SentiWordNet, and experiments were made on the {\textquotedblleft}IMDB movie review{\textquotedblright} dataset, with the three main approaches for sentiment analysis: lexicon-based, supervised machine learning and hybrids of the previous. Hybrid approaches performed the best, demonstrating the potential of merging knowledge bases and machine learning, but supervised approaches based on review embeddings were not far."
}