Knowledge-oriented Sentiment-level Embedding for Sentiment Classification
Authors: 
Xiaoran Xu, Pengfei LiAuthors Info & Claims
Xiaoran Xu, Pengfei LiAuthors Info & ClaimsPages 34 - 42
Abstract
Sentiment classification in document-level is an important task in Sentiment Analysis (SA). The existing methods learn mainly information from data for identifying the sentiment polarity of a document. We reveal that the sentiment information such as polarity can be an important external knowledge resource for classification. Our proposals are based on this idea and construct the models by employing hierarchical attention network and creating sentiment-level embedding. Furthermore, we also develop the attention mechanism by allowing it to receive both lexicon-level and sentiment-level information to improve the effectiveness of the model. The experiments on the IMDB movie reviews and Review Polarity 2.0 show good performance on document-level sentiment classification.
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Index Terms
- Knowledge-oriented Sentiment-level Embedding for Sentiment Classification
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Published In
December 2019
614 pages
ISBN:9781450372619
DOI:10.1145/3377713
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- Chinese Univ. of Hong Kong: Chinese University of Hong Kong
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Association for Computing Machinery
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Published: 07 February 2020
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ACAI 2019
ACAI 2019: 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence
December 20 - 22, 2019
Sanya, China
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ACAI '19 Paper Acceptance Rate 97 of 203 submissions, 48%;
Overall Acceptance Rate 173 of 395 submissions, 44%
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