Twitter Data-based Sarcastic Sentiment Analysis using Deep Learning Framework

Nowadays, due to rapid growth in web technologies and internet usage, there will be lots of data generated and available in the webpages. People are using social media websites to connect with each other and to exchange their knowledge, opinions and thoughts through Facebook, Instagram, Twitter and Google this are common platform to all. In the current generation, an individual opinion is expressed on social media. These views represent a variety of sentiments, which range from extreme positive to extreme negative. Most of the organizations leverage those views in an attempt to market their products better. In this paper, we proposed the approach to identify the sarcasm in the tweets. The approach preprocessed the tweets. Deep Learning frame-work is proposed to identify the sarcasm in the tweets. RNN and LSTM approaches are used in which depth wise separable convolutions are used to build lightweight deep neural networks for sarcasm detection. Glove model is used for vector representations for words. The approach used simple sentence patterns to determine sarcasm which is very challenging as the sentences are short and simple. The proposed model has obtained 92% accuracy.