language:
- en
- bem
- nya
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- text-classification
- aspect-based-sentiment-analysis
- absa
- low-resource
- zambia
baselineABSA
baselineABSA is a multilingual Aspect-Based Sentiment Analysis (ABSA) model for Zambian ride-hailing social-media reviews. It is a generic multilingual BERT model fine-tuned directly for aspect-conditioned sentiment classification, with no prior localization.
Task
The model performs aspect-conditioned sentiment classification. Given a review and a target service aspect, it predicts the sentiment expressed toward that specific aspect, rather than an overall sentiment for the whole review.
Sentiment classes: negative (0), neutral (1), positive (2).
Service aspects: driver_behavior, pricing, app_performance, payment, ride_quality, customer_support, service_quality, booking, safety, waiting_time.
Base model
google-bert/bert-base-multilingual-cased
Training data
The model was fine-tuned on a synthetic multilingual ride-hailing ABSA dataset containing English, Bemba_Cibemba, Nyanja_Cinyanja, and Lusaka_Slang reviews, with aspect-level sentiment annotations.
Training procedure
Standard fine-tuning of the full model. Training configuration: 7 epochs, learning rate 2e-5, train and evaluation batch size 16, weight decay 0.01, AdamW optimizer, evaluation and checkpoint saving per epoch, best checkpoint selected on macro F1-score.
Intended use
baselineABSA was developed as part of a master's dissertation on multilingual Aspect-Based Sentiment Analysis for low-resource Zambian ride-hailing social-media discourse. It serves as the baseline system in a comparative evaluation against ZambiaABSA, which uses an encoder adapted through ZambiaSocialBERT.
Limitations
The model was trained on synthetic data; performance on naturally occurring reviews may differ. Neutral sentiment and aspect entanglement remain difficult, as documented in the associated dissertation.
