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TDAM: a topic-dependent attention model for sentiment analysis
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Pergola, Gabriele, Gui, Lin and He, Yulan (2019) TDAM: a topic-dependent attention model for sentiment analysis. Information Processing & Management, 56 (6). 102084. doi:10.1016/j.ipm.2019.102084 ISSN 0306-4573.
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WRAP-TDAM-topic-dependent-attention-model-sentiment-analysis-Pergola-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1503Kb) | Preview |
Official URL: https://doi.org/10.1016/j.ipm.2019.102084
Abstract
We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and employs an attention mechanism to derive local topic embedding for words and sentences. These are subsequently incorporated in a modified Gated Recurrent Unit (GRU) for sentiment classification and extraction of topics bearing different sentiment polarities. Those topics emerge from the words' local topic embeddings learned by the internal attention of the GRU cells in the context of a multi-task learning framework. In this paper, we present the hierarchical architecture, the new GRU unit and the experiments conducted on users' reviews which demonstrate classification performance on a par with the state-of-the-art methodologies for sentiment classification and topic coherence outperforming the current approaches for supervised topic extraction. In addition, our model is able to extract coherent aspect-sentiment clusters despite using no aspect-level annotations for training.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Machine learning, Natural language processing (Computer science), Computational intelligence, Human-computer interaction, Neural networks (Computer science) | |||||||||
Journal or Publication Title: | Information Processing & Management | |||||||||
Publisher: | Elsevier | |||||||||
ISSN: | 0306-4573 | |||||||||
Official Date: | November 2019 | |||||||||
Dates: |
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Volume: | 56 | |||||||||
Number: | 6 | |||||||||
Article Number: | 102084 | |||||||||
DOI: | 10.1016/j.ipm.2019.102084 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 2 August 2019 | |||||||||
Date of first compliant Open Access: | 18 July 2020 | |||||||||
RIOXX Funder/Project Grant: |
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