SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis
2026-07-06 • Computation and Language
Computation and LanguageMachine Learning
AI summaryⓘ
The authors created SalAngaBhava, a new dataset for aspect-based sentiment analysis in Sinhala, a language mostly spoken in Sri Lanka. This dataset contains product reviews labeled with specific aspects and whether the sentiment is positive, negative, or neutral. The data was carefully collected and annotated to be consistent and balanced, covering many product areas. This resource helps researchers study fine-grained sentiments in Sinhala, which previously lacked such annotated data.
Sentiment AnalysisAspect-based Sentiment AnalysisNatural Language ProcessingSinhala LanguageDataset AnnotationLow-resource LanguagesProduct ReviewsSentiment Labeling
Authors
Lakshani Galwatta, Nisansa de Silva, Sarangi Aththanayake, Adithya Galwatta
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.