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Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Jointly Learning Bilingual Sentiment and Semantic Representations for Cross-Language Sentiment Classification

Authors : Huiwei Zhou, Yunlong Yang, Zhuang Liu, Yingyu Lin, Pengfei Zhu, Degen Huang

Published in: Information Retrieval

Publisher: Springer International Publishing

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Abstract

Cross-language sentiment classification (CLSC) aims at leveraging the semantic and sentiment knowledge in a resource-abundant language (source language) for sentiment classification in a resource-scarce language (target language). This paper proposes an approach to jointly learning bilingual semantic and sentiment representations (BSSR) for English-Chinese CLSC. First, two neural networks are adopted to learn sentence-level sentiment representations in English and Chinese views respectively, which are attached to all word semantic representations in the corresponding sentence to express the words in the certain sentiment context. Then, another two neural networks in two views are designed to jointly learn BSSR of the document from word representations concatenated with their sentence-level sentiment representations. The proposed approach could capture rich sentiment and semantic information in BSSR learning process. Experiments on NLP&CC 2013 CLSC dataset show that our approach is competitive with the state-of-the-art results.

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Metadata
Title
Jointly Learning Bilingual Sentiment and Semantic Representations for Cross-Language Sentiment Classification
Authors
Huiwei Zhou
Yunlong Yang
Zhuang Liu
Yingyu Lin
Pengfei Zhu
Degen Huang
Copyright Year
2017
Book
Information Retrieval

Print ISBN: 978-3-319-68698-1

Electronic ISBN: 978-3-319-68699-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68699-8_12