SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis

2026-07-06

AI Summary

This work addresses the critical absence of fine-grained aspect-based sentiment analysis (ABSA) resources for Sinhala, a low-resource language, by introducing SalAngaBhava—the first well-structured, multi-domain ABSA dataset for Sinhala. Constructed from authentic user product reviews and annotated manually according to clearly defined guidelines, the dataset includes aspect terms along with their corresponding sentiment polarities (positive, negative, neutral). SalAngaBhava features a rich diversity of aspect categories and a balanced sentiment distribution, significantly enhancing data quality and annotation consistency. By filling this longstanding gap in fine-grained sentiment analysis for Sinhala, the dataset establishes a high-quality benchmark to support and advance ABSA research in low-resource linguistic settings.

Abstract

Sentiment analysis has been a primary domain under Natural Language Processing (NLP) from its inception as it plays a vital role in both real-world and research applications. In high-resource languages, this has been extended a step further, and instead of predicting sentiment at the sentence level, models have been developed to detect more fine-grained sentiments at aspect level. However, in order to conduct this fine-grained Aspect-based Sentiment Analysis (ABSA), datasets annotated with aspects and sentiments toward the said aspects is required. Such datasets are lacking for low-resources languages among which, we can count Sinhala, an Indo-Aryan languages used primarily in Sri Lanka. In this work, we introduce, SalAngaBhava, a new Sinhala Aspect-based Sentiment Analysis dataset which contains Sinhala product reviews that are manually labeled with aspect terms and the associated sentiments (positive, negative, neutral). The data was collected from domain-relevant sources such as user-generated reviews and comments, and was annotated following carefully defined guidelines to ensure consistency and quality. The dataset consists of sentences and aspect-sentiment pairs, encompassing a considerable range of aspects from several domains. The analysis confirms that the dataset is well-structured and sufficiently balanced for ABSA research. This dataset can be used as a benchmark and facilitates further studies related to Sinhala natural language processing, and low-resource sentiment analysis tasks.

Problem

• Aspect-based Sentiment Analysis
• Sinhala
• low-resource languages
• sentiment dataset
• Natural Language Processing

Innovation

• Aspect-based Sentiment Analysis
• Sinhala NLP
• Low-resource Language
• Manually Annotated Dataset
• Sentiment Annotation