A Comprehensive Analysis on Sentiment Using Deep Learning and Transformers

Emotions describe the social attachment between the human that are ascendancy by cultural norms, social interactions, and Interpersonal bonds. So in this paper we are represent the application of deep learning models and transformers for emotion analysis on text based data. We are using different Word2Vec approaches for analyze the emotion from the textual data. As we know, Distributed Bag of Words (DBOW), Distributed Memory Concatenation (DMC), Distributed Memory Mean (DMM), and hybrid models DBOW+DMC and DBOW+DMM that’s all are the part of Word2Vec model and all are based on deep learning techniques. In this paper we also explore the NLP transformer such as BERT, BART, DistilBERT, RoBERTa, and GPT to analyze the emotion of data and compare the performance of deep learning based technique with transformers based techniques. The aim of the exploration is compare the effectiveness of these models using standard sentiment analysis datasets, assessing performance through accuracy metrics.