The purpose of text emotion analysis is to detect and recognize the classification of feeling expressed in text. In recent years, there has been an increase in text emotion analysis studies for English language since data were abundant. Due to the growth of social media large amount data are now available for regional languages such as Tamil and Sinhala as well. However, these languages lack necessary annotated corpus for many NLP tasks including emotion analysis. In this project, we present our scalable semi-automatic approach to create an annotated corpus named ACTSEA for Tamil and Sinhala to support emotion analysis. Alongside, our analysis on a sample of the produced data and the useful findings are presented for the low resourced NLP community to benefit. For ACTSEA, data were gathered from twitter platform and annotated manually after cleaning. We collected 600280 (Tamil) and 318308 (Sinhala) tweets in total which makes our corpus largest data collection which is currently available for these languages.
If you use this tool or dataset cite this paper.
ACTSEA: Annotated Corpus for Tamil & Sinhala Emotion Analysis
- Rajenthiran Jenarthanan
See also the list of contributors who participated in this project.
- Senate Research Committee (SRC) Grant funds awarded by the University of Moratuwa and funds from the Department of Official Languages of Sri Lanka
- R. Sridhar et al. research help to generate morphological keywords