Advertisement

Sentiment Analysis for Older People in Cross-Platform Instant Messaging Service

  • Haoran Xie
  • Tak-Lam Wong
  • Di ZouEmail author
  • Fu Lee Wang
  • Leung Pun Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10108)

Abstract

The population of older people increases in many developed and developing countries, so that the overall structures of the populations has been changing. However, older people are one of the most disadvantaged and vulnerable groups for digital exclusion in this technocratic society. Therefore, in this article, we aims to predict the sentiments for older people when they use the cross-platform instance messaging service such as WeChat or WhatsApp. Specifically, we adopt semi-annotation approaches to obtaining their sentimental labels from the textual data in the cross-platform instance messaging service. Furthermore, we propose a lexical-based framework for predicting the sentimental labels. The findings give us insight to develop applications for the inclusion of older people in digital world.

Keywords

Sentiment analysis Text mining Instance messaging service Active ageing Digital inclusion 

Notes

Acknowledgement

The work described in this paper was fully supported by a grant from Research Grants Council of Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14), the Internal Research Grant (RG 30/2014-2015) and the Start-Up Research Grant (RG 37/2016-2017R) of The Education University of Hong Kong.

References

  1. 1.
    Census and Statistics Department of Hong Kong. Thematic household survey report C Report No. 52 (2013)Google Scholar
  2. 2.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)Google Scholar
  3. 3.
    Berkowsky, R.W., Cotton, S.R., Yost, E.A., Winstead, V.P.: Attitudes towards and limitations to ICT use in assisted and independent living communities: findings from a specially-designed technological intervention. Educ. Gerontol. 39(11), 797–811 (2013)CrossRefGoogle Scholar
  4. 4.
    Boulton-Lewis, M.G., Buys, L., Lovie-Kitchin, J., Barnett, K., David, N.L.: Ageing, learning, and computer technology in Australia. Educ. Gerontol. 33(3), 253–270 (2007)CrossRefGoogle Scholar
  5. 5.
    Cattaneo, M., Malighetti, P., Spinelli, D.: The impact of university of the third age courses on ICT adoption. Comput. Hum. Behav. 63, 613–619 (2016)CrossRefGoogle Scholar
  6. 6.
    Davey, J.A.: Active ageing and education in mid and later life. Ageing Soc. 22(01), 95–113 (2002)CrossRefGoogle Scholar
  7. 7.
    Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 513–520 (2011)Google Scholar
  8. 8.
    Khvorostianov, N., Elias, N., Nimrod, G.: Without it I am nothing: the internet in the lives of older immigrants. New Media Soc. 14(4), 583–599 (2012)CrossRefGoogle Scholar
  9. 9.
    Koopman-Boyden, P.G., Reid, S.L.: Internet/e-mail usage and well-being among 65–84 year olds in New Zealand: policy implications. Educ. Gerontol. 35(11), 990–1007 (2009)CrossRefGoogle Scholar
  10. 10.
    Li, X., Xie, H., Rao, Y., Chen, Y., Liu, X., Huang, H., Wang, F.L.: Weighted multi-label classification model for sentiment analysis of online news. In: 2016 International Conference on Big Data and Smart Computing (BigComp), pp. 215–222. IEEE (2016)Google Scholar
  11. 11.
    Ng, H.T., Low, J.K.: Chinese part-of-speech tagging: one-at-a-time or all-at-once? Word-based or character-based? In: EMNLP, pp. 277–284 (2004)Google Scholar
  12. 12.
    Ortigosa, A., Martín, J.M., Carro, R.M.: Sentiment analysis in Facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014)CrossRefGoogle Scholar
  13. 13.
    Rao, Y., Xie, H., Li, J., Jin, F., Wang, L.F., Li, Q.: Social emotion classification of short text via topic-level maximum entropy model. Inf. Manag. 53(8), 978–986 (2016)CrossRefGoogle Scholar
  14. 14.
    Xie, H.-R., Li, Q., Cai, Y.: Community-aware resource profiling for personalized search in folksonomy. J. Comput. Sci. Technol. 27(3), 599–610 (2012)CrossRefzbMATHGoogle Scholar
  15. 15.
    Xie, H., Li, Q., Mao, X.: Context-aware personalized search based on user and resource profiles in folksonomies. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds.) APWeb 2012. LNCS, vol. 7235, pp. 97–108. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29253-8_9 CrossRefGoogle Scholar
  16. 16.
    Xie, H., Li, Q., Mao, X., Li, X., Cai, Y., Zheng, Q.: Mining latent user community for tag-based and content-based search in social media. Comput. J. 57(9), 1415–1430 (2014)CrossRefGoogle Scholar
  17. 17.
    Xie, H., Li, X., Wang, T., Chen, L., Li, K., Wang, F.L., Cai, Y., Li, Q., Min, H.: Personalized search for social media via dominating verbal context. Neurocomputing 172, 27–37 (2016)CrossRefGoogle Scholar
  18. 18.
    Xie, H., Li, X., Wang, T., Lau, R.Y.K., Wong, T.-L., Chen, L., Wang, F.L., Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy. Inf. Process. Manag. 52(1), 61–72 (2016)CrossRefGoogle Scholar
  19. 19.
    Xie, H., Zou, D., Lau, R.Y.K., Wang, F.L., Wong, T.-L.: Generating incidental word-learning tasks via topic-based and load-based profiles. IEEE Multimedia 23(1), 60–70 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Haoran Xie
    • 1
  • Tak-Lam Wong
    • 1
  • Di Zou
    • 2
    Email author
  • Fu Lee Wang
    • 3
  • Leung Pun Wong
    • 3
  1. 1.Department of Mathematics and Information TechnologyThe Education University of Hong KongTai PoHong Kong
  2. 2.English Language CentreThe Hong Kong Polytechnic UniversityKowloonHong Kong
  3. 3.Caritas Institute of Higher EducationTseung Kwan OHong Kong

Personalised recommendations