In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh most spoken language throughout the entire world. However, the language is structurally complicated, which makes this field arduous to extract emotions in an accurate manner. Several distinct approaches such as the extraction of positive and negative sentiments as well as multiclass emotions, have been implemented in this field of study. Nevertheless, the extraction of multiple sentiments is an almost untouched area in this language. Which involves identifying several feelings based on a single piece of text. Therefore, this study demonstrates a thorough method for constructing an annotated corpus based on scrapped data from Facebook to bridge the gaps in this subject area to overcome the challenges. To make this annotation more fruitful, the context-based approach has been used. Bidirectional Encoder Representations from Transformers (BERT), a well-known methodology of transformers, have been shown the best results of all methods implemented. Finally, a web application has been developed to demonstrate the performance of the pre-trained top-performer model (BERT) for multi-label ER in Bangla.

翻译：近年来，情感分析与情感识别在孟加拉语（全球使用人数第七多的语言）中日益流行。然而，该语言结构复杂，使得准确提取情感变得困难。该领域已实施多种不同方法，例如正面/负面情感提取和多类别情感识别等。然而，多情感提取在该语言中几乎仍是未涉足领域——即基于单篇文本识别多种情感。因此，本研究展示了一种基于Facebook爬取数据构建标注语料库的完整方法，以填补该领域的空白并克服挑战。为使标注更有效，采用了基于上下文的方法。在实现的所有方法中，Transformer的经典架构——双向编码器表示（BERT）展现出最佳效果。最后，开发了一款网络应用程序，以展示预训练最优模型（BERT）在孟加拉语多标签情感识别中的性能。