BEmoD: Development of Bengali Emotion Dataset for Classifying Expressions of Emotion in Texts

Multi-class Textual Emotion Categorization using Ensemble of Convolutional and Recurrent Neural Network

“…• Extended Bengali Emotion Dataset (EBEmoD) A Bengali emotion dataset contains six emotion classes (anger, disgust, fear, joy, sadness, surprise). We followed the standard steps (crawling, preprocessing, annotation, label verification) to create an emotion annotated dataset described by Das et al [7]. Ekman's [10] definition of emotion classes has been utilized to ensure the consistency of the samples in the dataset.…”

“…Ekman's [10] definition of emotion classes has been utilized to ensure the consistency of the samples in the dataset. In the previous work [7], the authors did not develop any model to classify emotion. This work extended their work by presenting an automatic emotion categorization system trained over a dataset of 9000 samples.…”

Classification of Textual Sentiment Using Ensemble Technique

“…Due to the unavailability of the benchmark textual sentiment datasets in Bengali, this work developed a dataset (i.e., BSaD) to perform the sentiment analysis task. We followed the directions suggested by Das et al [8] for developing the dataset. Following steps are carried out to prepare the BSaD:…”

Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus

“…High-quality Bangla-labeled data is nevertheless scarce in many sectors. Contrary to English and other Western dialects, which are acknowledged as rich dialects in terms of linguistics and technology, analyzing these massive volumes of data using NLP to identify underlying sentiments or emotions is a challenging research topic for resource-constrained languages like Bangla [11,17]. The highly inflected elements of the Indo-Aryan language, such as its 36 different noun forms, 24 different pronoun forms, and more than 160 varied verb forms, make the EC operation in Bangla exceptionally difficult.…”