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Reliability and Perceived Value of Sentiment Analysis for Twitter Data

In: Strategic Innovative Marketing

Author

Listed:
  • Jari Jussila

    (Tampere University of Technology)

  • Vilma Vuori

    (University of Vaasa)

  • Jussi Okkonen

    (University of Tampere)

  • Nina Helander

    (Tampere University of Technology)

Abstract

Social media offers rich data sources for companies that want to understand how they are perceived by their stakeholders. Sentiment analysis over Twitter can produce information about people’s feelings toward their brand, business, and directors (Saif et al. 2012). Based on this information, companies can take actions to enhance their customer experiences and perceived brand value. This study investigates the reliability and perceived value of two sentiment analysis tools developed to understand Finnish language, in contrast to human evaluators. For this purpose, a dataset of tweets from a Finnish software company was collected. For evaluating reliability Krippendorff’s α (Krippendorff 2007) is computed. Perceived value of the automatic and human evaluator classified sentiment is evaluated by interviewing the case company representatives. The results point out that the analysis carried out by the human evaluators was perceived more valuable by the company representatives than the automatic analysis, due to different granulation level of the analysis. Compared to the automatic analysis, the human evaluators were able to put the identified emotions from the tweets better into a context, which in turn diminished the potential misinterpretation of who was the target of the most negative tweets.

Suggested Citation

  • Jari Jussila & Vilma Vuori & Jussi Okkonen & Nina Helander, 2017. "Reliability and Perceived Value of Sentiment Analysis for Twitter Data," Springer Proceedings in Business and Economics, in: Androniki Kavoura & Damianos P. Sakas & Petros Tomaras (ed.), Strategic Innovative Marketing, pages 43-48, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-56288-9_7
    DOI: 10.1007/978-3-319-56288-9_7
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    Cited by:

    1. A. Fronzetti Colladon & F. Grippa & B. Guardabascio & G. Costante & F. Ravazzolo, 2021. "Forecasting consumer confidence through semantic network analysis of online news," Papers 2105.04900, arXiv.org, revised Jul 2023.
    2. A. Fronzetti Colladon & S. Grassi & F. Ravazzolo & F. Violante, 2020. "Forecasting financial markets with semantic network analysis in the COVID-19 crisis," Papers 2009.04975, arXiv.org, revised Jul 2023.
    3. Sanaz Ghorbanloo & Sajjad Shokouhyar, 2023. "Consumers' attitude footprint on sustainable development in developed and developing countries: a case study in the electronic industry," Operations Management Research, Springer, vol. 16(3), pages 1444-1475, September.

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