RGCN: Recurrent Graph Convolutional Networks for Target-Dependent Sentiment Analysis
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11775 LNAI, Page: 667-675
2019
- 13Citations
- 6Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
With the increasing numbers of user-generated content on the web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure, and prevalent approaches among them cannot effectively use the syntactic information. In this paper, we propose to use a novel framework named as recurrent graph convolutional network (RGCN) for target-dependent sentiment classification in which the given text is considered as a graph based on its syntactic structure and recurrent graph convolutional networks are used to encode the text and target. We conduct comprehensive experiments on publicly accessible datasets, and results demonstrate that our model outperforms the state-of-the-art baselines.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85081563079&origin=inward; http://dx.doi.org/10.1007/978-3-030-29551-6_59; http://link.springer.com/10.1007/978-3-030-29551-6_59; https://dx.doi.org/10.1007/978-3-030-29551-6_59; https://link.springer.com/chapter/10.1007/978-3-030-29551-6_59
Springer Science and Business Media LLC
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