Cross-genre Multi-label Emotion Classification on Mono-lingual and Code-mixed Texts

Abstract

This research focuses on This research focuses on Emotion Classification (EC) in textual data and explores the problem of Cross-genre Multi-Label Emotion Classification (CGMLEC). EC involves detecting the emotional attitude conveyed by text, while Multi-Label Emotion Classification aims to assign relevant emotion labels that accurately reflect the author's state of mind. In previous studies most of the work has been done using Same-genre Multi-label Emotion Classification [2], [14]–[16]. Whereas there is only one article that solves the problem of CGMLEC, but the language is the same. However, the problem of CGMLEC has not explored using mono-lingual (English tweets) and code-mixed (SMS messages). Secondly, this research study has Developed, Applied, Evaluated, and Compared Classical Machine Learning, Deep Learning and Transfer Learning based methods. Whereas in Transfer Learning methods we have implied 5 distinct sentence transformers (all-distilroberta-v1, all-MiniLM-L12-v2, all-mpnet-base-v2, facebook-dpr-question_encoder-single-nq-base, and LaBSE), Also the Feature Fussion based approach is applied which combines the features of all the transformers and then the Machine Learning models are applied. The proposed transformers-based approach outperforms with an F1 score of 0.3333 using all-distilroberta-v1 transformer.

) in textual data and explores the problem of Cross-genre Multi-Label Emotion Classification (CGMLEC). EC involves detecting the emotional attitude conveyed by text, while Multi-Label Emotion Classification aims to assign relevant emotion labels that accurately reflect the author's state of mind. In previous studies most of the work has been done using Same-genre Multi-label Emotion Classification [2], [14]–[16]. Whereas there is only one article that solves the problem of CGMLEC, but the language is the same. However, the problem of CGMLEC has not explored using mono-lingual (English tweets) and code-mixed (SMS messages). Secondly, this research study has Developed, Applied, Evaluated, and Compared Classical Machine Learning, Deep Learning and Transfer Learning based methods. Whereas in Transfer Learning methods we have implied 5 distinct sentence transformers (all-distilroberta-v1, all-MiniLM-L12-v2, all-mpnet-base-v2, facebook-dpr-question_encoder-single-nq-base, and LaBSE), Also the Feature Fussion based approach is applied which combines the features of all the transformers and then the Machine Learning models are applied. The proposed transformers-based approach outperforms with an F1 score of 0.3333 using all-distilroberta-v1 transformer.