@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://aclanthology.org/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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T GenABSA-Vec: Generative Aspect-Based Sentiment Feature Vectorization for Document-Level Sentiment Classification
%A Minkang, Liu
%A Suet Yan, Jasy Liew
%Y Baldwin, Tim
%Y Rodríguez Méndez, Sergio José
%Y Kuo, Nicholas
%S Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association
%D 2024
%8 December
%I Association for Computational Linguistics
%C Canberra, Australia
%F minkang-suet-yan-2024-genabsa
%X 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.
%U https://aclanthology.org/2024.alta-1.3/
%P 30-40
Markdown (Informal)
[GenABSA-Vec: Generative Aspect-Based Sentiment Feature Vectorization for Document-Level Sentiment Classification](https://aclanthology.org/2024.alta-1.3/) (Minkang & Suet Yan, ALTA 2024)
ACL