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Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences

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  • Vasile-Daniel Păvăloaia

    (Department of Accounting, Business Information Systems and Statistics, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700506 Iaşi, Romania)

  • Elena-Mădălina Teodor

    (Web Department, Falcon Trading Company, 700521 Iaşi, Romania)

  • Doina Fotache

    (Department of Accounting, Business Information Systems and Statistics, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700506 Iaşi, Romania)

  • Magdalena Danileţ

    (Department of Management, Marketing and Business Administration, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700506 Iaşi, Romania)

Abstract

Any brand’s presence on social networks has a significant impact on emotional reactions of its users to different types of posts on social media (SM). If a company understands the preferred types of posts (photo or video) of its customers, based on their reactions, it could make use of these preferences in designing its future communication strategy. The study examines how the use of SM technology and customer-centric management systems could contribute to sustainable business development of companies by means of social customer relationship management (sCRM). The two companies included in the study provide a general consumer good in the beverage industry. As such, it may be said that users interacting with the posts these companies make on their official channels are in fact customers or potential customers. The study aims to analyze customer reaction to two types of posts (photos or videos) on six social networks: Facebook, Twitter, Instagram, Pinterest, Google+ and Youtube. It brings evidence on the differences and similarities between the SM customer behaviors of two highly competitive brands in the beverage industry. Drawing on current literature on SM, sCRM and marketing, the output of this study is the conceptualization and measurement of a brand’s SM ability to understand customer preferences for different types of posts by using various statistical tools and the sentiment analysis (SA) technique applied to big sets of data.

Suggested Citation

  • Vasile-Daniel Păvăloaia & Elena-Mădălina Teodor & Doina Fotache & Magdalena Danileţ, 2019. "Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences," Sustainability, MDPI, vol. 11(16), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4459-:d:258567
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    References listed on IDEAS

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    5. Myriam Caratù & Valerio Brescia & Ilaria Pigliautile & Paolo Biancone, 2023. "Assessing Energy Communities’ Awareness on Social Media with a Content and Sentiment Analysis," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    6. Ismail Badache & Adrian-Gabriel Chifu & Sébastien Fournier, 2022. "Unsupervised and Supervised Methods to Estimate Temporal-Aware Contradictions in Online Course Reviews," Mathematics, MDPI, vol. 10(5), pages 1-24, March.
    7. Carlos Ruiz-Núñez & Ivan Herrera-Peco & Silvia María Campos-Soler & Álvaro Carmona-Pestaña & Elvira Benítez de Gracia & Juan José Peña Deudero & Andrés Ignacio García-Notario, 2023. "Sentiment Analysis on Twitter: Role of Healthcare Professionals in the Global Conversation during the AstraZeneca Vaccine Suspension," IJERPH, MDPI, vol. 20(3), pages 1-13, January.
    8. Laith T. Khrais, 2020. "Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce," Future Internet, MDPI, vol. 12(12), pages 1-14, December.

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