[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83928-en":3,"doc-seo-83928-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83928,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis","Sentiment analysis in natural language processing benefits from aspect-level modeling, but fine-grained Aspect-based Sentiment Analysis (ABSA) requires datasets annotated with both aspect terms and the corresponding sentiments. SalAngaBhava introduces a new Sinhala ABSA dataset built from Sinhala product reviews and code-mixed comments, manually labeled using detailed, linguistically informed guidelines. The dataset contains sentences and aspect–sentiment pairs spanning multiple domains and includes positive, negative, and neutral sentiments, structured for balanced, benchmark-ready research.","SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis  \nLakshani Galwatta∗ , Nisansa de Silva∗ , Sarangi Aththanayake∗ , Adithya Galwatta†  \n∗ Dept. of Computer Science & Engineering, University of Moratuwa, Sri Lanka.  \n{lakshani.25, NisansaDds, [sarangi](sarangi}@cse.mrt.ac.lk)[}](sarangi}@cse.mrt.ac.lk)[@cse.mrt.ac.lk](sarangi}@cse.mrt.ac.lk)  \n†School of Technologies, Cardiff Metropolitan university, United Kingdom.  \n[st20315307@outlook.cardiffmet.ac.uk](st20315307@outlook.cardiffmet.ac.uk)  \narXiv :2607 .05259v 1 [ cs .CL] 6 Jul 2026  \nAbstract—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.  \nIndex Terms—Aspect-based Sentiment Analysis, Sinhala, Lowresource Language, Aspect Extraction, Sentiment Classification, Dataset Annotation  \nI. INTRODUCTION  \nSentiment Analysis is a fundamental activity of Natural Language Processing (NLP) which involves the detection and understanding of subjective data in written format, such as opinions, emotions, and attitudes [1–4] . It has become a critical part of a broad spectrum of real-life applications such as product review analysis, social media monitoring, customer feedback analysis, and public opinion mining. Conventional methods of sentiment analysis often provide one sentiment label, e.g. positive, negative, or neutral, to a whole document or sentence [5] . Although useful in a wide range of cases, this coarse-grained analysis may dramatically fail when it comes to handling complex opinions that are presented in the natural language given that there are many different things that can be said about an entity, and one can have varying degrees of sentiment [1] .  \nAspect-based Sentiment Analysis (ABSA) has been suggested to overcome these shortcomings as a fine-grained sen-  \ntiment analysis. Instead of creating a single overall sentiment label, ABSA aims to find specific aspects or features referred to in the text and the sentiment polarity that they have [1, 4] . For example, a Sinhala product review may simultaneously express a positive opinion about a product’s display and sound quality while conveying a negative sentiment about its battery life, each representing a distinct aspect–sentiment pair, as illustrated in Fig 1. ABSA allow breaking down opinions into aspect-sentiment pairs, which can be more detailed and actionable feedback on users. Consequently, the topic of ABSA has received high research activity, which has given rise to benchmark datasets and evaluation campaigns, especially on high-resource languag","cbCaijRNqeTlWVx7","https://ap.wps.com/l/cbCaijRNqeTlWVx7","pdf",751149,1,9,"English","en",105,"# Introduction\n## Background and motivation\n## Dataset contribution and availability\n# Task Descriptions\n## ABSA use cases for Sinhala","[{\"question\":\"Why is aspect-based sentiment analysis needed instead of sentence-level sentiment labels?\",\"answer\":\"Sentence-level sentiment can fail for complex opinions where different parts of an entity receive different sentiment polarities. ABSA breaks opinions into aspect–sentiment pairs to provide more detailed feedback.\"},{\"question\":\"What does the SalAngaBhava dataset include?\",\"answer\":\"SalAngaBhava contains Sinhala product reviews (including code-mixed reviews) with sentences and manually labeled aspect–sentiment pairs covering positive, negative, and neutral sentiments.\"},{\"question\":\"How was the dataset annotation ensured for quality and consistency?\",\"answer\":\"Annotations were carried out by native Sinhala speakers using a carefully developed, linguistically informed annotation plan with elaborate guidelines aimed at consistency and reliability.\"}]",1784191494,23,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"salangabhava-a-sinhala-market-dataset-for-aspect-based-sentiment-analysis","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/salangabhava-a-sinhala-market-dataset-for-aspect-based-sentiment-analysis/83928/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is aspect-based sentiment analysis needed instead of sentence-level sentiment labels?","Question",{"text":75,"@type":76},"Sentence-level sentiment can fail for complex opinions where different parts of an entity receive different sentiment polarities. ABSA breaks opinions into aspect–sentiment pairs to provide more detailed feedback.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the SalAngaBhava dataset include?",{"text":80,"@type":76},"SalAngaBhava contains Sinhala product reviews (including code-mixed reviews) with sentences and manually labeled aspect–sentiment pairs covering positive, negative, and neutral sentiments.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the dataset annotation ensured for quality and consistency?",{"text":84,"@type":76},"Annotations were carried out by native Sinhala speakers using a carefully developed, linguistically informed annotation plan with elaborate guidelines aimed at consistency and reliability.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]