Abstract
The semantic analysis is an important tool for processing people’s opinions, but processing data from social networking sites like Twitter is still challenging. Transit related tweets in London and Prague collected during the COVID-19 pandemic were analyzed using two corpus-based approaches – Bag-of-Words and Latent Dirichlet Allocation. Punctuality was the most frequent issue in both cities, followed by COVID-19 in London and Comfort in Prague. Analysis for the busiest London station enhanced the importance of the Breakdowns topic. Specific issues were found for some stations such as Victoria Station in London. The BoW method in our cases provides more robust results, namely for large heterogeneous samples, while LDA is well-suited for topic extraction using narrow well-specified samples focused on the explored theme.
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Acknowledgements
This research was funded by the grant SP2023/023 of the Faculty of Mining and Geology of the Technical University of Ostrava “Possibilities of using artificial intelligence in geodata science for the purpose of predicting real estate prices” and grant “Podpora vědy a výzkumu v Moravskoslezském kraji 2022” of the Faculty of Mining and Geology of the Technical University of Ostrava.
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Zajac, M., Horak, J., Kukuliac, P. (2023). Semantic Analysis of Transit Related Tweets in London and Prague. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_31
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