TY - GEN
T1 - Cross-lingual sentiment relation capturing for cross-lingual sentiment analysis
AU - Chen, Qiang
AU - Li, Wenjie
AU - Lei, Yu
AU - Liu, Xule
AU - Luo, Chuwei
AU - He, Yanxiang
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Bi-View CNN
KW - Cross-lingual sentiment analysis
KW - Cross-lingual sentiment relation
UR - https://www.scopus.com/pages/publications/85018713570
U2 - 10.1007/978-3-319-56608-5_5
DO - 10.1007/978-3-319-56608-5_5
M3 - Conference article published in proceeding or book
SN - 9783319566078
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 54
EP - 67
BT - Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings
PB - Springer Verlag
T2 - 39th European Conference on Information Retrieval, ECIR 2017
Y2 - 8 April 2017 through 13 April 2017
ER -