Sentiment analysis for older people in cross-platform instant messaging service

Haoran XIE, Tak-lam WONG, Di ZOU*, Fu Lee WANG, Leung Pun WONG

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Research

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationEmerging Technologies for Education : First International Symposium, SETE 2016 held in conjunction with ICWL 2016, Rome, Italy, October 26–29, 2016, revised selected papers
EditorsTing-Ting WU, Rosella GENNARI, Yueh-Min HUANG, Haoran XIE, Yiwei CAO
PublisherSpringer International Publishing AG
Number of pages5
ISBN (Electronic)9783319528366
ISBN (Print)9783319528359
Publication statusPublished - 2017
Externally publishedYes
Event1st International Symposium on Emerging Technologies for Education - Sapienza University, Rome, Italy
Duration: 26 Oct 201629 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10108 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st International Symposium on Emerging Technologies for Education
Abbreviated titleSETE 2016

Bibliographical note

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.


  • Sentiment analysis
  • Text mining
  • Instance messaging service
  • Active ageing
  • Digital inclusion


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