BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories
This work addresses the lack of emotion analysis resources for Hindi, a low-resource language, by providing a new annotated corpus, though it is incremental as it applies existing methods to new data.
The authors introduced BHAAV, the first and largest Hindi text corpus for emotion analysis, containing 20,304 sentences from 230 short stories across 18 genres, annotated into five emotion categories by native speakers. They trained baseline classifiers and reported their performance on this dataset.
In this paper, we introduce the first and largest Hindi text corpus, named BHAAV, which means emotions in Hindi, for analyzing emotions that a writer expresses through his characters in a story, as perceived by a narrator/reader. The corpus consists of 20,304 sentences collected from 230 different short stories spanning across 18 genres such as Inspirational and Mystery. Each sentence has been annotated into one of the five emotion categories - anger, joy, suspense, sad, and neutral, by three native Hindi speakers with at least ten years of formal education in Hindi. We also discuss challenges in the annotation of low resource languages such as Hindi, and discuss the scope of the proposed corpus along with its possible uses. We also provide a detailed analysis of the dataset and train strong baseline classifiers reporting their performances.