Birleşik Krallik'taki Toplu Taşima Hakkindaki Kamuoyu Görüşleri: Duygu Analizi ve Konu Modellemesi

Translated title of the contribution: Public Opinion on UK Public Transportation Through Sentiment Analysis and Topic Modeling

Asligul Aksan, Hatice Camgöz Akdaǧ

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Translated title of the contributionPublic Opinion on UK Public Transportation Through Sentiment Analysis and Topic Modeling
Original languageTurkish
Title of host publication31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350343557
DOIs
Publication statusPublished - 2023
Event31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Turkey
Duration: 5 Jul 20238 Jul 2023

Publication series

Name31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Conference

Conference31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Country/TerritoryTurkey
CityIstanbul
Period5/07/238/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Fingerprint

Dive into the research topics of 'Public Opinion on UK Public Transportation Through Sentiment Analysis and Topic Modeling: Duygu Analizi ve Konu Modellemesi'. Together they form a unique fingerprint.

Cite this