SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis
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 l
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- PossiblePossibly related (embedding) · 46%aadya940/numpyai →
- LinkedLinked via arxiv author · 85%Lakshani Galwatta →
“SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis”
- LinkedLinked via arxiv author · 85%Nisansa de Silva →
“SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis”
- LinkedLinked via arxiv author · 85%Sarangi Aththanayake →
“SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis”
- LinkedLinked via arxiv author · 85%Adithya Galwatta →
“SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis”