Target-Oriented Data Annotation for Emotion and Sentiment Analysis in Tourism Related Social Media Data

Alireza Alaei, Ying Wang, Vinh Bui, Bela Stantic

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Social media have been a valuable data source for studying people’s opinions, intentions, and behaviours. Such a data source incorporating advanced big data analysis methods, such as machine-operated emotion and sentiment analysis, will open unprecedented opportunities for innovative data-driven destination monitoring and management. However, a big challenge any machine-operated text analysis method faces is the ambiguity of the natural languages, which may cause an expression to have different meanings in different contexts. In this work, we address the ambiguity challenge by proposing a context-aware dictionary-based target-oriented emotion and sentiment analysis method that incorporates inputs from both humans and machines to introduce an alternative approach to measuring emotions and sentiment in limited tourism-related data. The study makes a methodological contribution by creating a target dictionary specifically for tourism sentiment analysis. To demonstrate the performance of the proposed method, a case of target-oriented emotion and sentiment analysis of posts from Twitter for the Gold Coast of Australia as a tourist destination was considered. The results suggest that Twitter data cover a broad range of destination attributes and can be a valuable source for comprehensive monitoring of tourist experiences at a destination.

Original languageEnglish
Article number150
JournalFuture Internet
Volume15
Issue number4
DOIs
Publication statusPublished - Apr 2023

Keywords

  • big data analysis
  • data annotation
  • data-driven destination management
  • emotion detection
  • social media data
  • target-oriented sentiment analysis

ASJC Scopus subject areas

  • Computer Networks and Communications

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