Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India
Abstract Owing to the onset of the new media age, the idea of e-public participation has proven to be a great complement to the limitations of the conventional public participation approach. In this respect, location-based social networks (LBSN) data can prove to be a game shift in this digital era...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-10-01
|
| Series: | Computational Urban Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43762-022-00066-7 |
| _version_ | 1811250032061448192 |
|---|---|
| author | Apoorv Agrawal Paulose N. Kuriakose |
| author_facet | Apoorv Agrawal Paulose N. Kuriakose |
| author_sort | Apoorv Agrawal |
| collection | DOAJ |
| description | Abstract Owing to the onset of the new media age, the idea of e-public participation has proven to be a great complement to the limitations of the conventional public participation approach. In this respect, location-based social networks (LBSN) data can prove to be a game shift in this digital era to offer an insight into the commuter perception of service delivery. The paper aims to investigate the potential of using Twitter data to assess commuters’ perceptions of the Delhi metro, India, by presenting a comprehensive methodology for extracting, processing, and interpreting the data. The study extracts Twitter data from the official handle of the Delhi metro, performs semantic and sentiment analysis to comprehend commuters’ concerns and assesses commuters’ sentiments on the predicted concerns. The paper outlines that the current depth of Twitter data is more inclined to instantaneous responses to grievances encountered. Moreover, the analysis presents that for the data extraction period, the topics ‘Ride Safety’ and ‘Crowding’ have the lowest scores, while ‘Personnel Attitude’ and ‘Customer Interface’ have the highest scores. Further, the paper highlights insights gleaned from Twitter data in addition to the aspects included in the conventional satisfaction survey. The paper concludes by outlining the opportunities and limitations of LBSN analytics for effective public transportation decision-making in India. |
| first_indexed | 2024-04-12T15:58:01Z |
| format | Article |
| id | doaj.art-2fc6ea24703a441ba1da76249f4ff4a0 |
| institution | Directory Open Access Journal |
| issn | 2730-6852 |
| language | English |
| last_indexed | 2024-04-12T15:58:01Z |
| publishDate | 2022-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj.art-2fc6ea24703a441ba1da76249f4ff4a02022-12-22T03:26:18ZengSpringerComputational Urban Science2730-68522022-10-012111210.1007/s43762-022-00066-7Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in IndiaApoorv Agrawal0Paulose N. Kuriakose1Department of Urban & Regional Planning, School of Planning and Architecture BhopalDepartment of Urban & Regional Planning, School of Planning and Architecture BhopalAbstract Owing to the onset of the new media age, the idea of e-public participation has proven to be a great complement to the limitations of the conventional public participation approach. In this respect, location-based social networks (LBSN) data can prove to be a game shift in this digital era to offer an insight into the commuter perception of service delivery. The paper aims to investigate the potential of using Twitter data to assess commuters’ perceptions of the Delhi metro, India, by presenting a comprehensive methodology for extracting, processing, and interpreting the data. The study extracts Twitter data from the official handle of the Delhi metro, performs semantic and sentiment analysis to comprehend commuters’ concerns and assesses commuters’ sentiments on the predicted concerns. The paper outlines that the current depth of Twitter data is more inclined to instantaneous responses to grievances encountered. Moreover, the analysis presents that for the data extraction period, the topics ‘Ride Safety’ and ‘Crowding’ have the lowest scores, while ‘Personnel Attitude’ and ‘Customer Interface’ have the highest scores. Further, the paper highlights insights gleaned from Twitter data in addition to the aspects included in the conventional satisfaction survey. The paper concludes by outlining the opportunities and limitations of LBSN analytics for effective public transportation decision-making in India.https://doi.org/10.1007/s43762-022-00066-7Commuters’ perceptionSemantic analysisSentiment analysisLatent Dirichlet Allocation (LDA)Bidirectional Encoder Representations from Transformers (BERT) |
| spellingShingle | Apoorv Agrawal Paulose N. Kuriakose Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India Computational Urban Science Commuters’ perception Semantic analysis Sentiment analysis Latent Dirichlet Allocation (LDA) Bidirectional Encoder Representations from Transformers (BERT) |
| title | Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India |
| title_full | Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India |
| title_fullStr | Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India |
| title_full_unstemmed | Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India |
| title_short | Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India |
| title_sort | implications of a twitter data centred methodology for assessing commuters perceptions of the delhi metro in india |
| topic | Commuters’ perception Semantic analysis Sentiment analysis Latent Dirichlet Allocation (LDA) Bidirectional Encoder Representations from Transformers (BERT) |
| url | https://doi.org/10.1007/s43762-022-00066-7 |
| work_keys_str_mv | AT apoorvagrawal implicationsofatwitterdatacentredmethodologyforassessingcommutersperceptionsofthedelhimetroinindia AT paulosenkuriakose implicationsofatwitterdatacentredmethodologyforassessingcommutersperceptionsofthedelhimetroinindia |