Enhancing Multilingual Sentiment Analysis with Explainability for Sinhala, English, and Code-Mixed Content

• URL: http://arxiv.org/abs/2504.13545v1
• Date: Fri, 18 Apr 2025 08:21:12 GMT
• Title: Enhancing Multilingual Sentiment Analysis with Explainability for Sinhala, English, and Code-Mixed Content
• Authors: Azmarah Rizvi, Navojith Thamindu, A. M. N. H. Adhikari, W. P. U. Senevirathna, Dharshana Kasthurirathna, Lakmini Abeywardhana,
• Abstract summary: Existing models struggle with low-resource languages like Sinhala and lack interpretability for practical use.<n>This research develops a hybrid aspect-based sentiment analysis framework that enhances multilingual capabilities with explainable outputs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sentiment analysis is crucial for brand reputation management in the banking sector, where customer feedback spans English, Sinhala, Singlish, and code-mixed text. Existing models struggle with low-resource languages like Sinhala and lack interpretability for practical use. This research develops a hybrid aspect-based sentiment analysis framework that enhances multilingual capabilities with explainable outputs. Using cleaned banking customer reviews, we fine-tune XLM-RoBERTa for Sinhala and code-mixed text, integrate domain-specific lexicon correction, and employ BERT-base-uncased for English. The system classifies sentiment (positive, neutral, negative) with confidence scores, while SHAP and LIME improve interpretability by providing real-time sentiment explanations. Experimental results show that our approaches outperform traditional transformer-based classifiers, achieving 92.3 percent accuracy and an F1-score of 0.89 in English and 88.4 percent in Sinhala and code-mixed content. An explainability analysis reveals key sentiment drivers, improving trust and transparency. A user-friendly interface delivers aspect-wise sentiment insights, ensuring accessibility for businesses. This research contributes to robust, transparent sentiment analysis for financial applications by bridging gaps in multilingual, low-resource NLP and explainability.

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