Sarcasm Detection with BiLSTM Multihead Attention

Sarcasm detection is a difficult task in natural language processing because it requires interpreting nuanced details and contextual indications that often challenge simple explanation. This research explores the implementation of a sarcasm detection model using advanced ways, involving Bidirectional Long Short-Term Memory(BiLSTM) networks and Multihead Attention mechanisms. The bidirectional flow of information in the BiLSTM allows the model to capture dependencies in both directions, aiding in the understanding of long-term contextual connections. The model’s capacity to focus on different parts of the input sequence is further improved with the addition of Multihead Attention, resulting in a more complex contextual representation. In prior research, a Multihead attention-based bidirectional long-short memory (MHA-BiLSTM) network had been built to detect sarcastic remarks in a given corpus. To increase the model performance, we perform fine tunning, varying the hyperparameters using low learning rate optimizer and sigmoid activation function. Results show that our model give better results than previous MHA-BilSTM model. We’ll look at misclassifications to show the model’s advantages and possible drawbacks. The findings contribute to the ongoing efforts to develop robust sarcasm detection models, providing insights into the intricate nature of language comprehension and interpretation.