Analysing Public Transport User Sentiment on Low Resource Multilingual Data
محفوظ في:
| الحاوية / القاعدة: | arXiv.org (Dec 9, 2024), p. n/a |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , |
| منشور في: |
Cornell University Library, arXiv.org
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full text outside of ProQuest |
| الوسوم: |
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3143052562 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3143052562 | ||
| 045 | 0 | |b d20241209 | |
| 100 | 1 | |a Myoya, Rozina L | |
| 245 | 1 | |a Analysing Public Transport User Sentiment on Low Resource Multilingual Data | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 9, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Public transport systems in many Sub-Saharan countries often receive less attention compared to other sectors, underscoring the need for innovative solutions to improve the Quality of Service (QoS) and overall user experience. This study explored commuter opinion mining to understand sentiments toward existing public transport systems in Kenya, Tanzania, and South Africa. We used a qualitative research design, analysing data from X (formerly Twitter) to assess sentiments across rail, mini-bus taxis, and buses. By leveraging Multilingual Opinion Mining techniques, we addressed the linguistic diversity and code-switching present in our dataset, thus demonstrating the application of Natural Language Processing (NLP) in extracting insights from under-resourced languages. We employed PLMs such as AfriBERTa, AfroXLMR, AfroLM, and PuoBERTa to conduct the sentiment analysis. The results revealed predominantly negative sentiments in South Africa and Kenya, while the Tanzanian dataset showed mainly positive sentiments due to the advertising nature of the tweets. Furthermore, feature extraction using the Word2Vec model and K-Means clustering illuminated semantic relationships and primary themes found within the different datasets. By prioritising the analysis of user experiences and sentiments, this research paves the way for developing more responsive, user-centered public transport systems in Sub-Saharan countries, contributing to the broader goal of improving urban mobility and sustainability. | |
| 651 | 4 | |a South Africa | |
| 651 | 4 | |a Kenya | |
| 653 | |a Data analysis | ||
| 653 | |a Qualitative analysis | ||
| 653 | |a Datasets | ||
| 653 | |a Public transportation | ||
| 653 | |a Minibuses | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Clustering | ||
| 653 | |a Transportation systems | ||
| 653 | |a User experience | ||
| 653 | |a Multilingualism | ||
| 653 | |a Natural language processing | ||
| 653 | |a Qualitative research | ||
| 653 | |a Vector quantization | ||
| 700 | 1 | |a Marivate, Vukosi | |
| 700 | 1 | |a Idris Abdulmumin | |
| 773 | 0 | |t arXiv.org |g (Dec 9, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3143052562/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.06951 |