Abstract:
Emotion encompasses a wide range of human feelings. People express their emotions using a
combination of facial expressions, body language, words, pictures, videos, texts, etc. Text
messages accompanied by emojis and emoticons are commonly utilized in modern longdistance communication through social media platforms and A single line comment can convey
multiple emotions. Sentiment analysis and emotion recognition are crucial problems in the field
of natural language processing (NLP) and have extensive applications in several domains such
as artificial intelligence, e-commerce, healthcare, education, human-computer interaction
(HCI), business, and politics. Despite Bangla being the 7th most widely spoken language
globally, it is classified as a low-resource language due to the scarcity of digital resources and
linguistic tools. Authors have used several methods such as machine learning, deep learning,
rule-based approaches, keyword-based approaches and hybrid techniques to extract emotions
from text and emojis. In this study, we have used a scraping algorithm for collecting Bangla
text data containing emojis from social media and construct an annotated corpus based on the
scraped data. We have developed a comprehensive framework for recognizing multilabel
emotions from text data that contains emojis. In this study, hybrid models and conventional
machine learning techniques have been used for multilabel emotion recognition. Support
Vector Machine outperforms other methods for individual emotion classes with 70.4%
accuracy.
