Sentiment Analysis for Older People in Cross-Platform Instant Messaging Service
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.
KeywordsSentiment analysis Text mining Instance messaging service Active ageing Digital inclusion
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.
- 1.Census and Statistics Department of Hong Kong. Thematic household survey report C Report No. 52 (2013)Google Scholar
- 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
- 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
- 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.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