Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Constructing a Chinese Conversation Corpus for Sentiment Analysis

Authors : Yujun Zhou, Changliang Li, Bo Xu, Jiaming Xu, Lei Yang, Bo Xu

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Sentiment analysis plays an important role in many applications. This paper introduces our ongoing work related to the sentiment analysis on Chinese conversation. The main purpose is to construct a Chinese conversation corpus for sentiment analysis and provide a benchmark result on this corpus. To explore the effectiveness of machine learning based approaches for sentiment analysis on Chinese conversation, we firstly collected conversational data from some online English learning websites and our instant messages, and manually annotated it with three sentiment polarities and 22 fine-grained emotion classes. Then we applied multiple representative classification methods to evaluate the corpus. The evaluation results provide good suggestions for the future research. And we will release the corpus with gold standards publicly for research purposes.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Homographic Puns Recognition Based on Latent Semantic Structures
next chapter Improving Retrieval Quality Using PRF Mechanism from Event Perspective
Footnotes
Literature
1.
Husin, N., Abdullah, M.T., Mahmod, R.: A systematic literature review for topic detection in chat conversation for cyber-crime investigation. Int. J. Digit. Content Technol. Appl. 8(3), 22 (2014)
2.
Zhao, Y., Qin, B., Liu, T.: Creating a fine-grained corpus for chinese sentiment analysis. IEEE Intell. Syst. 30(1), 36–43 (2015)CrossRef
3.
Zhang, L., Chen, C.: Sentiment classification with convolutional neural networks: an experimental study on a large-scale Chinese conversation corpus. In: 12th International Conference on Computational Intelligence and Security (CIS 2016), pp. 165–169 (2016)
4.
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP 2013, vol. 1631, p. 1642 (2013)
5.
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd ACL, pp. 655–665 (2014)
6.
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: EMNLP 2015, pp. 1422–1432 (2015)
7.
Chen, H., Sun, M., Tu, C., Lin, Y., Liu, Z.: Neural sentiment classification with user and product attention. In: EMNLP 2016, pp. 1650–1659 (2016)
8.
Quan, C., Ren, F.: Construction of a blog emotion corpus for Chinese emotional expression analysis. In: EMNLP 2009, pp. 1446–1454 (2009)
9.
Li, C., Xu, B., Wu, G., He, S., Tian, G., Hao, H.: Recursive deep learning for sentiment analysis over social data. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 1, pp. 180–185 (2014)
10.
Liao, X., Li, B., Xu, L.: Overview of topic-based Chinese message polarity classification in SIGHAN 2015. In: ACL-IJCNLP 2015, p. 56 (2015)
11.
Peng, H., Cambria, E., Hussain, A.: A review of sentiment analysis research in Chinese language. Cogn. Comput. 9(4), 1–13 (2017)CrossRef
12.
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: AAAI 2015, pp. 2267–2273 (2015)
13.
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. CoRR abs/1607.01759 (2016)
14.
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: NIPS 2015, pp. 649–657 (2015)
15.
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP 2014, pp. 1746–1751 (2014)
16.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: NAACL HLT 2016 (2016)
17.
Zhou, Y., Xu, J., Cao, J., Xu, B., Li, C., Xu, B.: Hybrid attention networks for Chinese short text classification. In: CICLing 2017 (2017)
18.
Zhou, Y., Xu, B., Xu, J., Yang, L., Li, C., Xu, B.: Compositional recurrent neural networks for Chinese short text classification. In: 2016 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 137–144 (2016)
Metadata
Title
Constructing a Chinese Conversation Corpus for Sentiment Analysis
Authors
Yujun Zhou
Changliang Li
Bo Xu
Jiaming Xu
Lei Yang
Bo Xu
Copyright Year
2018
Book
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-319-73617-4

Electronic ISBN: 978-3-319-73618-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-73618-1_48

Premium Partner

    Image Credits