Kāvi: An Annotated Corpus of Punjabi Poetry with Emotion Detection Based on ‘Navrasa’

Abstract

As the poetries are penned with numerous emotions and feelings, the computational linguistic analysis for emotion detection from poetry is a very challenging task. The main aim of this work is to detect emotions from Punjabi poetries based on different features present in Punjabi poetries. This is a pioneering approach in emotion detection from Punjabi poetries. ‘Kāvi’ Punjabi poetry corpus was manually annotated based on the Indian concept of ‘Navrasa’. In this corpus, 948 poetries were classified into 9 emotion states as presented in ‘Navrasa’, namely ‘karuna’, ‘shringar’, ‘hasya’, ‘raudra’, ‘veer’, ‘bhayanak’, ‘vibhata’, ‘adbhut’, and ‘shaanti’. This corpus was manually annotated and the Kappa Fleiss index was calculated for inter-annotator agreement. The different features (linguistic, poetic and statistical) were used for building the classifier and the two machine learning techniques, Naïve Bayes (NB) and Support Vector Machine (SVM) were experimented with these features. It was found that with an accuracy of 70.02%, SVM improved the overall accuracy of the emotion classification task. Further, Poetic features were found to outperform the linguistic features for the emotion detection task.