BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories

TL;DR

This paper introduces BHAAV, the first large Hindi text corpus for emotion analysis in stories, annotated with five emotion categories, enabling research in emotion detection for low-resource languages.

Contribution

The paper presents the creation of BHAAV, a large annotated Hindi corpus for emotion analysis, along with baseline classifiers and insights into annotation challenges in low-resource languages.

Findings

BHAAV contains 20,304 sentences from 230 stories across 18 genres.

Baseline classifiers achieve promising performance on emotion detection.

Discussion on annotation challenges in low-resource language datasets.

Abstract

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.