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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

A Joint Model for Sentiment Classification and Opinion Words Extraction

verfasst von : Dawei Cong, Jianhua Yuan, Yanyan Zhao, Bing Qin

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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Abstract

In recent years, mining opinions from customer reviews has been widely explored. Aspect-level sentiment analysis is a fine-grained subtask, which aims to detect the sentiment polarity towards a particular target in a sentence. While most previous works focus on sentiment polarity classification, opinion words towards the target are also very important for that they provide details about target and contribute to judging polarity. To this end, we propose a hierarchical network for jointly modeling aspect-level sentiment classification and word-level opinion words extraction. Our joint model acquires superior performance in opinion words extraction and achieves comparable results in sentiment polarity classification on two datasets from SemEval 2014.

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Vorheriges Kapitel Review Headline Generation with User Embedding
Nächstes Kapitel Network Representation Learning Based on Community and Text Features
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Metadaten
Titel
A Joint Model for Sentiment Classification and Opinion Words Extraction
verfasst von
Dawei Cong
Jianhua Yuan
Yanyan Zhao
Bing Qin
Copyright-Jahr
2018
Buch
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-030-01715-6

Electronic ISBN: 978-3-030-01716-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01716-3_28