TITLE: Anubhuti -- An annotated dataset for emotional analysis of Bengali short stories 

ABSTRACT: Thousands of short stories and articles are being written in many different
languages all around the world today. Bengali, or Bangla, is the second highest
spoken language in India after Hindi and is the national language of the
country of Bangladesh. This work reports in detail the creation of Anubhuti --
the first and largest text corpus for analyzing emotions expressed by writers
of Bengali short stories. We explain the data collection methods, the manual
annotation process and the resulting high inter-annotator agreement of the
dataset due to the linguistic expertise of the annotators and the clear
methodology of labelling followed. We also address some of the challenges faced
in the collection of raw data and annotation process of a low resource language
like Bengali. We have verified the performance of our dataset with baseline
Machine Learning as well as a Deep Learning model for emotion classification
and have found that these standard models have a high accuracy and relevant
feature selection on Anubhuti. In addition, we also explain how this dataset
can be of interest to linguists and data analysts to study the flow of emotions
as expressed by writers of Bengali literature.
Thousands of short stories and articles are being written in many different
languages all around the world today. Bengali, or Bangla, is the second highest
spoken language in India after Hindi and is the national language of the
country of Bangladesh. This work reports in detail the creation of Anubhuti --
the first and largest text corpus for analyzing emotions expressed by writers
of Bengali short stories. We explain the data collection methods, the manual
annotation process and the resulting high inter-annotator agreement of the
dataset due to the linguistic expertise of the annotators and the clear
methodology of labelling followed. We also address some of the challenges faced
in the collection of raw data and annotation process of a low resource language
like Bengali. We have verified the performance of our dataset with baseline
Machine Learning as well as a Deep Learning model for emotion classification
and have found that these standard models have a high accuracy and relevant
feature selection on Anubhuti. In addition, we also explain how this dataset
can be of interest to linguists and data analysts to study the flow of emotions
as expressed by writers of Bengali literature.
Anubhuti -- An annotated dataset for emotional analysis of Bengali short stories
http://arxiv.org/abs/2010.03065v1