A netnographic-based semantic analysis of tweet contents for stress management

Jari Jussila, Eman Alkhammash, Norah Saleh Alghamdi, Prashanth Madhala, Mohammad Ayoub Khan

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Social media platforms provide new value for markets and research companies. This article explores the use of social media data to enhance customer value propositions. The case study involves a company that develops wearable Internet of Things (IoT) devices and services for stress management. Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users' stress management practices. The aim is to analyze the tweets about stress management practices and to identify the context from the tweets. Thereafter, we map the tweets on pleasure and arousal to elicit customer insights. We analyzed a case study of a marketing strategy on the Twitter platform. Participants in the marketing campaign shared photos and texts about their stress management practices. Machine learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign participants. The computational semantic analysis of the tweets was compared to the text analysis of the tweets. The content analysis of only tweet images resulted in 96% accuracy in detecting tweet context, while that of the textual content of tweets yielded an accuracy of 91%. Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

Original languageEnglish
Pages (from-to)1845-1856
Number of pages12
JournalComputers, Materials and Continua
Issue number1
Publication statusPublished - 2021
Publication typeA1 Journal article-refereed


  • Context recognition
  • Semantic analysis
  • Social media
  • Stress
  • Twitter

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Biomaterials
  • Modelling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering


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