Research Project
Multi-Pipeline Sentiment Analysis
West-African Pidgin language processing and analysis with advanced machine learning techniques.
Project Overview
This research introduces a multi-pipeline approach to Sentiment Analysis, focusing on improving the accuracy and relevance of results for West-African pidgin language. Existing approaches to sentiment analysis of West-African pidgin have been fragmented, often training a new model with pidgin data, and focusing on general sentiment polarity.
Key Achievements
- • Developed a holistic multi-pipeline system for exhaustive polarity analysis
- • Achieved F1-score of 74.5% with AfriBerta model (average over 5 runs)
- • Subject classifier achieved 81% accuracy in identifying relevant text
- • Integrated cross-lingual model with Roberta (XLM-R) using transfer learning
Research Methodology
Multi-Pipeline Architecture
The system employs a two-stage approach: first, a subject classifier determines if the text is relevant to the target subject matter using Logistic Regression. Then, for relevant texts, a fine-tuned XLM-R model performs sentiment analysis. This approach significantly improves both accuracy and computational efficiency by filtering out irrelevant content before detailed analysis.
Data Collection and Processing
Twitter data was collected and processed into tokens for training and evaluation. The dataset was carefully curated to represent the linguistic diversity of West-African pidgin, ensuring the model's robustness across different regional variations and usage patterns.
Model Development
The sentiment analysis component uses a cross-lingual model with Roberta (XLM-R) that was fine-tuned and expanded through transfer learning in the AfriBerta model. This approach leverages pre-trained multilingual representations while adapting to the specific characteristics of West-African pidgin.
Authors
Primary Authors
- • Segun Aina
- • Joshua Etim
- • Seun Ayeni
- • Aderonke Lawal
- • Oluwatoyin Odukoya
Affiliation
Omolara Ogungbe
Obafemi Awolowo University
Department of Computer Science
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