@inproceedings{minkang-suet-yan-2024-genabsa,
    title = "{G}en{ABSA}-Vec: Generative Aspect-Based Sentiment Feature Vectorization for Document-Level Sentiment Classification",
    author = "Minkang, Liu  and
      Suet Yan, Jasy Liew",
    editor = "Baldwin, Tim  and
      Rodr{\'i}guez M{\'e}ndez, Sergio Jos{\'e}  and
      Kuo, Nicholas",
    booktitle = "Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2024",
    address = "Canberra, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.alta-1.3/",
    pages = "30--40",
    abstract = "Currently, document-level sentiment classification focuses on extracting text features directly using a deep neural network and representing the document through a high-dimensional vector. Such sentiment classifiers that directly accept text as input may not be able to capture more fine-grained sentiment representations based on different aspects in a review, which could be informative for document-level sentiment classification. We propose a method to construct a GenABSA feature vector containing five aspect-sentiment scores to represent each review document. We first generate an aspect-based sentiment analysis (ABSA) quadruple by finetuning the T5 pre-trained language model. The aspect term from each quadruple is then scored for sentiment using our sentiment lexicon fusion approach, SentLex-Fusion. For each document, we then aggregate the sentiment score belonging to the same aspect to derive the aspect-sentiment feature vector, which is subsequently used as input to train a document-level sentiment classifier. Based on a Yelp restaurant review corpus labeled with sentiment polarity containing 2040 documents, the sentiment classifier trained with ABSA features aggregated using geometric mean achieved the best performance compared to the baselines."
}