---
---
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.
---


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