Özet
Social media has become a valuable data source for gathering and analyzing public opinion on products and services. Among the popular social media platforms, Twitter stands out for its ability to provide place-time information in a text format called tweets. In this study, sentiment analysis and topic modeling of tweets related to public transportation in the United Kingdom were analyzed. Using the Robustly Optimized BERT Pretraining Approach (RoBERTa), tweets are divided according to their polarities: positive, neutral, and negative. Additionally, Latent Dirichlet Allocation (LDA) is applied to positive and negative tweets, and topics providing the causes are obtained. These topics reveal the strengths and weaknesses of the United Kingdom's public transportation service.
Tercüme edilen katkı başlığı | Public Opinion on UK Public Transportation Through Sentiment Analysis and Topic Modeling |
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Orijinal dil | Türkçe |
Ana bilgisayar yayını başlığı | 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 |
Yayınlayan | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Elektronik) | 9798350343557 |
DOI'lar | |
Yayın durumu | Yayınlandı - 2023 |
Etkinlik | 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Turkey Süre: 5 Tem 2023 → 8 Tem 2023 |
Yayın serisi
Adı | 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 |
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???event.eventtypes.event.conference??? | 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 |
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Ülke/Bölge | Turkey |
Şehir | Istanbul |
Periyot | 5/07/23 → 8/07/23 |
Bibliyografik not
Publisher Copyright:© 2023 IEEE.
Keywords
- public transportation
- sentiment analysis
- topic modelling