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

• URL: http://arxiv.org/abs/2607.05259v1
• Date: Mon, 06 Jul 2026 16:05:26 GMT
• Title: SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis
• Abstract summary: This work introduces SalAngaBhava, a new Sinhala Aspect-based Sentiment Analysis dataset.<n>The data was collected from domain-relevant sources such as user-generated reviews and comments.<n>The analysis confirms that the dataset is well-structured and sufficiently balanced for ABSA research.
• Score: 1.0232954388448412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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