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- W2907508720 abstract "Sentence-level sentiment classification aims to mine fine-grained sentiment information from texts. Existing methods for this task are usually based on supervised learning and rely on massive labeled sentences for model training. However, annotating sufficient sentences is expensive and time-consuming. In this paper, we propose a neural sentence-level sentiment classification approach which can exploit heterogeneous sentiment supervision and reduce the dependence on labeled sentences. Besides the sentence-level supervision from labeled sentences, our approach can also incorporate the word-level supervision extracted from sentiment lexicons, document-level supervision extracted from labeled documents and sentiment relations between sentences extracted from unlabeled documents. A unified neural framework is proposed to fuse heterogeneous sentiment supervision to train sentence-level sentiment classification model. Experiments on benchmark datasets validate the effectiveness of our approach." @default.
- W2907508720 created "2019-01-11" @default.
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- W2907508720 date "2018-11-01" @default.
- W2907508720 modified "2023-10-16" @default.
- W2907508720 title "Neural Sentence-Level Sentiment Classification with Heterogeneous Supervision" @default.
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- W2907508720 doi "https://doi.org/10.1109/icdm.2018.00194" @default.
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