Summary of Analysing Public Transport User Sentiment on Low Resource Multilingual Data, by Rozina L. Myoya et al.
Analysing Public Transport User Sentiment on Low Resource Multilingual Data
by Rozina L. Myoya, Vukosi Marivate, Idris Abdulmumin
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to improve public transportation systems in Sub-Saharan countries is presented, focusing on commuter opinion mining to enhance Quality of Service (QoS) and overall user experience. The study analyzes sentiments towards existing public transport systems in Kenya, Tanzania, and South Africa using Multilingual Opinion Mining techniques, addressing linguistic diversity and code-switching. Natural Language Processing (NLP) tools such as AfriBERTa, AfroXLMR, AfroLM, and PuoBERTa are employed for sentiment analysis. The results reveal predominantly negative sentiments in South Africa and Kenya, while the Tanzanian dataset shows mainly positive sentiments due to advertising tweets. Feature extraction using Word2Vec model and K-Means clustering highlights semantic relationships and primary themes across datasets. This research paves the way for developing more responsive, user-centered public transport systems, contributing to urban mobility and sustainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Public transportation in some African countries is not well taken care of. The authors wanted to know what people think about their public transport systems. They looked at Twitter messages from Kenya, Tanzania, and South Africa to see if people like or dislike the buses, trains, and mini-bus taxis they use every day. They used special tools that can understand many languages to analyze the feelings behind these tweets. The results show that most people in South Africa and Kenya don’t like their public transport systems, but people in Tanzania seem happy because they were tweeting about ads! By understanding what people think, we can make better transportation systems that fit our needs. |
Keywords
» Artificial intelligence » Clustering » Feature extraction » K means » Natural language processing » Nlp » Word2vec
