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@article{181011,
        author = {Ms. Anjali Kiran Yadav},
        title = {Emotion Detection in Social Media Posts Using Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3397-3398},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181011},
        abstract = {With  the  increasing  use  of  social  media  as  
it is a  platform  for  self-expression,  emotion  detection  
from user-generated content has become a valuable 
tool for understanding public mood and behavior. This 
paper proposes a deep learning approach for detecting 
multiple emotions-such as anger, joy, sadness, fear, and 
surprise-from textual data extracted from Twitter. We 
employ pre-trained transformer models such as BERT 
and Roberta and compare their performance with 
traditional 
LSTM-based models. The results 
demonstrate the superiority of transformer-based 
architectures in capturing nuanced emotional 
expressions, even in noisy and informal text data.},
        keywords = {},
        month = {June},
        }