• CH.Madhavi Sudha
• , K.Ganesh
• B.Sai nath

Abstract— The technique of following a real-life scenario, extracting emotion from unstructured text is an active and challenging area of research. It has diverse applications in various aspects of our daily life To overcome various challenges involved in detecting emotion from text, researchers from diverse fields applied various Machine learning algorithms i.e knn algorithm. However, deep learning methods such as Bi Directional Long Short-Term Memory are effective to detect emotion by maintaining the sequence structure of the text. In this work, we use Bi-directional long short-term memory with attention layer for emotion detection for better accuracy for prediction.In addition, we employ a text preprocessing method to improve further results. We perform the experiments on three data and the models are evaluated based on the classification accuracy.

Machine learning. Deep learning, K nearest neighbour, Emotion Detection, Bi directional LSTM,

"Tracking Attention of Social Media Events using Bi-LSTM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.c573-c576, June-2022, Available :http://www.jetir.org/papers/JETIR2206270.pdf

"Tracking Attention of Social Media Events using Bi-LSTM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppc573-c576, June-2022, Available at : http://www.jetir.org/papers/JETIR2206270.pdf