An Overview of Business Intelligence Framework for Sentiment Analysis of United Airlines Using Data from Social Media

Authors

  • Marvin Imbin De Castro University of Santo Tomas

DOI:

https://doi.org/10.58776/ijitcsa.v3i3.224

Keywords:

sentiment analysis, stream analytics, twitter, united airlines

Abstract

Understanding customer sentiment in real time has become increasingly critical for service-oriented industries, particularly airlines operating within highly competitive and socially sensitive environments. This study proposes an integrated Business Intelligence (BI) framework for sentiment analysis of United Airlines using social media data sourced from Twitter. The framework aims to transform large volumes of unstructured, high-velocity text data into actionable insights that support informed decision-making, customer experience enhancement, and brand reputation management. The proposed architecture incorporates sequential analytical components including data ingestion, preprocessing, natural language processing, machine learning–based sentiment classification, and BI-driven visualization. Modern text analytics techniques such as tokenization, lemmatization, and vectorization are applied to prepare textual content for polarity detection, while supervised learning algorithms are evaluated to classify sentiment into positive, negative, and neutral categories. The study outlines the rationale for adopting a scalable, cloud-compatible architecture that supports both batch and stream processing to accommodate the dynamic nature of social media data. Key implementation challenges—such as handling noisy and ambiguous text, managing evolving linguistic patterns, overcoming API rate limitations, and ensuring data quality—are examined. The paper further discusses best practices to mitigate these challenges, including robust data-cleaning pipelines, periodic model retraining, careful feature engineering, and the incorporation of governance principles for ethical data use. The results demonstrate that integrating sentiment analytics within a BI context enables organizations such as United Airlines to monitor customer perceptions more effectively and respond proactively to emerging issues. The framework provides a practical foundation for organizations seeking to operationalize social media analytics for strategic and operational decision support.

References

. A. Ohlheiser, “The full timeline of how social media turned United into the biggest story in the country,” The Washington Post, Apr. 11, 2017. [Online]. Available: https://www.washingtonpost.com/news/the-intersect/wp/2017/04/11/the-full-timeline-of-how-social-media-turned-united-into-the-biggest-story-in-the-country/

. A. Sharma and U. Ghose, “Sentimental analysis of Twitter data with respect to general elections in India,” Procedia Computer Science, vol. 173, pp. 325–334, 2020, doi: 10.1016/j.procs.2020.06.038.

. A. Sarlan, C. Nadam, and S. Basri, “Twitter sentiment analysis,” in Proc. Int. Conf. Information Technology and Multimedia (ICIMU), 2014.

. V. A. Kharde and S. S. Sonawane, “Sentiment analysis of Twitter data: A survey of techniques,” International Journal of Computer Applications, vol. 139, no. 11, 2016.

. K. Alizade, “Limitations of Twitter Data,” Towards Data Science, 2021. [Online]. Available: https://towardsdatascience.com/limitations-of-twitter-data-94954850cacf

. O. Harfoushi, D. Hasan, and R. Obiedat, “Sentiment analysis algorithms through Azure Machine Learning: Analysis and comparison,” Modern Applied Science, vol. 12, no. 7, p. 49, 2018, doi: 10.5539/mas.v12n7p49.

. D. Corn, “Hey @ United, a family member just flew across the country on your airline,” Twitter, Dec. 9, 2020. [Online]. Available: https://twitter.com/DavidCornDC/status/1336361619107049472

. B. Zhang, “The 11 best and worst airlines in America,” Business Insider, 2018. [Online]. Available: https://www.businessinsider.com/best-worst-airlines-america-consumer-reports-2018-3

. M. Matousek, “United Airlines CEO Oscar Munoz admits strained employee relations,” Business Insider, Mar. 2018. [Online]. Available: https://www.businessinsider.com

. M. Matousek, “United Airlines replaces employee bonuses with lottery system,” Business Insider, Mar. 2018. [Online]. Available: https://www.businessinsider.com

. M. Matousek, “United’s new bonus lottery sparks backlash from employees,” Business Insider, 2018. [Online]. Available: https://www.businessinsider.com

. M. Matousek, “Employees express distrust after United bonus controversy,” Business Insider, 2018. [Online]. Available: https://www.businessinsider.com

. Oracle Corporation, “Oracle Cloud Infrastructure Streaming—Overview,” Oracle Cloud Documentation, 2023. [Online]. Available: https://docs.oracle.com/en/cloud/

. Tableau Software, “Tableau Platform Overview,” Tableau Documentation, 2023. [Online]. Available: https://www.tableau.com/

. Tableau Software, “Tableau Streaming and Real-Time Analytics,” Tableau Help, 2023. [Online]. Available: https://help.tableau.com/

. C. J. Hutto and E. Gilbert, “VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text,” in Proc. Eighth Int. AAAI Conf. Weblogs and Social Media (ICWSM), 2014. [Online]. Available: https://ojs.aaai.org/index.php/ICWSM/article/view/14550

. P. S. Beri, “Sentiment Analysis using VADER,” Medium, 2020. [Online]. Available: https://medium.com/

. M. Beck, “How to scrape tweets from Twitter,” Medium, 2021. [Online]. Available: https://towardsdatascience.com/how-to-scrape-tweets-from-twitter-59287e20f0f1

Downloads

Published

18-12-2025

How to Cite

De Castro, M. I. (2025). An Overview of Business Intelligence Framework for Sentiment Analysis of United Airlines Using Data from Social Media. International Journal of Information Technology and Computer Science Applications, 3(3), 111–117. https://doi.org/10.58776/ijitcsa.v3i3.224