Abstract
To extract real-time information referring to a specific place from social network service texts such as tweets, it is necessary to analyze the temporal semantics of the reference. To solve this problem, we created a corpus with multiple annotations for more than 10,000 tweets using crowdsourcing. We constructed an automatic analysis model based on multiple neural networks and compared their characteristics. Our dataset and codes are released in our website (http://www.cl.ecei.tohoku.ac.jp/~matsuda/TA_corpus/).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We used the following 10 nouns as targets: “Akihabara”,“Kiyomizu-dera (Kiyomizu Temple)”, “Shibuya-eki (Shibuya station)”,“Sky Tree”, “Sendai”,“shiyakusyo (city hall)”, “kousaten (crossing)”,“byouin (hospital)”, “kaisatsu (ticket gate)” and“doubtsuen (zoo)”.
- 2.
In Japanese, “zoo” is also used as a metaphor to indicate a lively appearance.
References
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: Proceedings of the ACL (2014)
Huang, R., Cases, I., Jurafsky, D., Condoravdi, C., Riloff, E.: Distinguishing past, on-going, and future events: the eventstatus corpus. In: EMNLP (2016)
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the ACL-HLT (2011)
Johnson, R., Zhang, T.: Effective use of word order for text categorization with convolutional neural networks (2014). arXiv preprint arXiv:1412.1058
Kim, Y.: Convolutional neural networks for sentence classification (2014). arXiv preprint arXiv:1408.5882
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2014). http://arxiv.org/abs/1412.6980
Li, C., Sun, A.: Fine-grained location extraction from tweets with temporal awareness. In: Proceedings of the SIGIR (2014)
Pustejovsky et al.: The timebank corpus. In: Corpus Linguistics, vol. 2003, p. 40 (2003)
Saurí, R., Pustejovsky, J.: Are you sure that this happened? Assessing the factuality degree of events in text. Comput. Linguist. 38(2), 261–299 (2012). https://doi.org/10.1162/COLI_a_00096
Severyn, A., Moschitti, A.: Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the SIGIR (2015)
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING 2016, The 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307. The COLING 2016 Organizing Committee, Osaka, Japan, December 2016. http://aclweb.org/anthology/C16-1311
Verhagen, M., Sauri, R., Caselli, T., Pustejovsky, J.: Semeval-2010 task 13: Tempeval-2. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 57–62. Association for Computational Linguistics (2010)
Vo, D.T., Zhang, Y.: Target-dependent twitter sentiment classification with rich automatic features. In: Proceedings of the IJCAI (2015)
Acknowledgments
This work was partially supported by Research and Development on Real World Big Data Integration and Analysis, MEXT, Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Matsuda, K., Sango, M., Okazaki, N., Inui, K. (2018). Monitoring Geographical Entities with Temporal Awareness in Tweets. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-77116-8_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77115-1
Online ISBN: 978-3-319-77116-8
eBook Packages: Computer ScienceComputer Science (R0)