Sentiment analysis about airport ground access using social media

Authors

  • Carolina Silva Ansélmo Aeronautics Institute of Technology, São Paulo – Brazil
  • Giovanna Miceli Ronzani Borille Aeronautics Institute of Technology, São Paulo – Brazil
  • Anderson Ribeiro Correia Aeronautics Institute of Technology, São Paulo – Brazil

DOI:

https://doi.org/10.14295/transportes.v30i1.2515

Keywords:

Air transport, Ground access, Social Network Twitter

Abstract

An adequate airport ground access system is relevant for a good level of service, and it is essential to identify the user's perception of the available means of transport. Sentiment analysis techniques and machine learning have been identified as positive and negative sentences with user-generated content on the social network Twitter. From March 2018 to December 2019, we collected spontaneous opinions about the case study of GRU Airport (SBGR). The tweets surveyed were related to the terms: airport, Guarulhos, and means of transport: transport apps of urban mobility, bus, taxi, train, and private vehicles. Trains had a greater quantity of tweets, with the main reason for dissatisfaction related to the location of the airport station. In addition, the indicators positively evaluated were services availability, cost, and journey time. The Naïve Bayes machine learning technique showed an accuracy of 82.14% and a precision of 88.14% for classifying tweets into positive or negative perceptions. The results obtained can be valuable to government entities, influencing the level of service offered. The content generated on social media can be useful in several areas of knowledge, complementing field research and helping to develop new research methods and data analysis.

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Published

2022-04-13

How to Cite

Silva Ansélmo, C. ., Ronzani Borille, G. M. ., & Ribeiro Correia, A. . (2022). Sentiment analysis about airport ground access using social media. Transportes, 30(1), 2515. https://doi.org/10.14295/transportes.v30i1.2515

Issue

Vol. 30 No. 1 (2022)

Section

Articles

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Copyright (c) 2022 Carolina Silva Ansélmo, Giovanna Miceli Ronzani Borille, Anderson Ribeiro Correia

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