An Ensemble Framework with Optimal Features for Sarcasm Detection in Social Media Data
Keywords:
Activation function, Feature Selection, Neural Networks, Optimization, Sarcasm DetectionAbstract
The main objective of the proposed work is to identify sarcasm in social media data. Sarcasm is being used to degrade or convey contempt in writing and speaking. Sarcasm has been studied extensively in NLP. Feature selection and the nature of classifiers plays a major role in detecting sarcasm. Effective feature selection increases the accuracy of the sarcasm detection. The work has developed an Ensemble framework with optimal features for sarcasm detection in social media data .The framework consists of four important components i.e. Pre-processing, Feature extraction, Optimal Feature selection and Sarcasm detection. From the extracted features, the most optimal features has been selected and thereafter the training as well as testing is performed using a new optimized CNN model by adding projected weight function of CNN. The developed CNN is fine-tuned by standard AptenodytesForsteri Optimization Algorithm (AFO).The sarcasm detection is carried out using a new ensemble technique that has been constructed with optimized Recurrent Neural Network (RNN) deep learning classifier. To enhance the sarcasm detection accuracy the activation function of RNN is optimized via Firebug Swarm Optimization (FSO). An ensemble framework has been developed and the outcome shows the comparison result of proposed FSOCNN with existing algorithms such as Recurrent neural network, Convolutional neural network and Artificial neural network is 97.72% and the proposed method's performance has been compared to other existing models and represented the outcomes in accuracy, specificity, and F measure.
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