Sentiment Analysis and Opinion Mining
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Abstract
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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@article{Galwatta2026SalAngaBhava,
title = {SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis},
author = {Lakshani Galwatta and Nisansa de Silva and Sarangi Aththanayake and Adithya Galwatta},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2607.05259},
url = {https://doi.org/10.48550/arxiv.2607.05259}
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