From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation

• URL: http://arxiv.org/abs/2011.03021v1
• Date: Thu, 5 Nov 2020 18:28:13 GMT
• Title: From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation
• Authors: Patrick Huber and Giuseppe Carenini
• Abstract summary: We propose a novel framework to exploit task-related discourse for the task of sentiment analysis. More specifically, we are combining the large-scale, sentiment-dependent MEGA-DT treebank with a novel neural architecture for sentiment prediction. Experiments show that our framework using sentiment-related discourse augmentations for sentiment prediction enhances the overall performance for long documents.
• Score: 30.615883375573432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sentiment analysis, especially for long documents, plausibly requires methods capturing complex linguistics structures. To accommodate this, we propose a novel framework to exploit task-related discourse for the task of sentiment analysis. More specifically, we are combining the large-scale, sentiment-dependent MEGA-DT treebank with a novel neural architecture for sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model. Experiments show that our framework using sentiment-related discourse augmentations for sentiment prediction enhances the overall performance for long documents, even beyond previous approaches using well-established discourse parsers trained on human annotated data. We show that a simple ensemble approach can further enhance performance by selectively using discourse, depending on the document length.

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