Abstract
Sentiment connection is the basis of cross-lingual sentiment analysis (CSLA) solutions. Most of existing work mainly focus on general semantic connection that the misleading information caused by non-sentimental semantics probably lead to relatively low efficiency. In this paper, we propose to capture the document-level sentiment connection across languages (called cross-lingual sentiment relation) for CLSA in a joint two-view convolutional neural networks (CNNs), namely Bi-View CNN (BiVCNN). Inspired by relation embedding learning, we first project the extracted parallel sentiments into a bilingual sentiment relation space, then capture the relation by subtracting them with an error-tolerance. The bilingual sentiment relation considered in this paper is the shared sentiment polarity between two parallel texts. Experiments conducted on public datasets demonstrate the effectiveness and efficiency of the proposed approach.
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Notes
- 1.
NLP&CC is an annual conference of Chinese information technology professional committee organized by Chinese Computer Federation (CCF). For more details, please refer to: http://tcci.ccf.org.cn/conference/2013/dldoc/evdata03.zip.
- 2.
Word2Vec is one of the models implemented in the free python library Gensim: http://pypi.python.org/pypi/gensim.
- 3.
The pre-trained word embedding models and the Weibo posts leveraged are available at: https://drive.google.com/open?id=0B0l0oLL2GUuoblNta0QyY1BkdGM.
- 4.
The parameter setting used in this paper is ‘-s 7’.
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Acknowledge
We thank all the anonymous reviewers for their detailed and insightful comments on this paper. The work described in this paper was supported by National Natural Science Foundation of China (61272291, 61672445, 61472290 and 61472291) and The Hong Kong Polytechnic University (G-YBP6, 4-BCB5 and B-Q46C).
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Chen, Q., Li, W., Lei, Y., Liu, X., Luo, C., He, Y. (2017). Cross-Lingual Sentiment Relation Capturing for Cross-Lingual Sentiment Analysis. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_5
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