Abstract
Sentiment analysis is important to develop marketing strategies, enhance sales and optimize supply chain for electronic commerce. Many supervised and unsupervised algorithms have been applied to build the sentiment analysis model, which assume that the distributions of the labeled and unlabeled data are identical. In this paper, we aim to deal with the issue of a classifier trained for use in one domain might not perform as well in a different one, especially when the distribution of the labeled data is different with that of the unlabeled data. To tackle this problem, we incorporate feature extraction methods into the neural network model for cross-domain sentiment classification. These methods are applied to simplify the structure of the neural network and improve the accuracy. Experiments on two real-world datasets validate the effectiveness of our methods for cross-domain sentiment classification.
Keywords
The authors contributed equally to this work.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Pan, S.J., Ni, X., Sun, J.-T., Yang, Q., Chen, Z.: Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th International Conference on World Wide Web, pp. 751–760 (2010)
Zhang, Y., Zhang, N., Si, L., Lu, Y., Wang, Q., Yuan, X.: Cross-domain and cross-category emotion tagging for comments of online news. In: Proceedings of the 37th International ACM SIGIR Conference, pp. 627–636 (2014)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79–86 (2002)
Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using em. Mach. Learn. 39(2–3), 103–134 (2000)
Morchid, M., Dufour, R., Linares, G.: Topic-space based setup of a neural network for theme identification of highly imperfect transcriptions. In: IEEE Automatic Speech Recognition and Understanding Workshopp (2015)
Francesca, F., Zanzotto, F.M.: SVD feature selection for probabilistic taxonomy learning. In: Proceedings of the Workshop on Geometrical Models of Natural Language Semantics, pp. 66–73 (2009)
Dasgupta, A., Drineas, P., Harb, B., Josifovski, V., Mahoney. M.W.: Feature selection methods for text classification. In: Proceedings of the 13th ACM SIGKDD International Conference, pp. 230–239 (2007)
Rao, Y., Lei, J., Liu, W., Li, Q., Chen, M.: Building emotional dictionary for sentiment analysis of online news. World Wide Web J. 17, 723–742 (2014)
Rao, Y., Li, Q., Liu, W., Wu, Q., Quan, X.: Affective topic model for social emotion detection. Neural Netw. 58, 29–37 (2014)
Rao, Y., Li, Q., Mao, X., Liu, W.: Sentiment topic models for social emotion mining. Inf. Sci. 266, 90–100 (2014)
Dai, W., Xue, G.-R., Yang, Q., Yu, Y.: Co-clustering based classification for out-of-domain documents. In: Proceedings of the 13th ACM SIGKDD International Conference, pp. 210–219 (2007)
Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: Proceedings of the 9th ACM SIGKDD International Conference, pp. 89–98 (2003)
Rao, Y.: Contextual sentiment topic model for adaptive social emotion classification. IEEE Intell. Syst. 31(1), 41–47 (2016)
Strapparava, C., Mihalcea, R.: Semeval- task 14: affective text. In: Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 70–74 (2007)
Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: International Joint Conference on Neural Networks, pp. 593–605 (1989)
Kurkova, V., Kainen, P.C., Kreinovich, V.: Estimates of the number of hidden units and variation with respect to half-spaces. Neural Netw. 10(6), 1061–1068 (1997)
Acknowledgements
This research has been substantially supported by a grant from the Soft Science Research Project of Guangdong Province (Grant No. 2014A030304013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhu, E., Huang, G., Mo, B., Wu, Q. (2016). Features Extraction Based on Neural Network for Cross-Domain Sentiment Classification. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-32055-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32054-0
Online ISBN: 978-3-319-32055-7
eBook Packages: Computer ScienceComputer Science (R0)