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An Ensemble Model for detecting Sarcasm on Social Media

Nowadays, sarcasm is extensively utilized in regular conversations over social media sites. There have been cases where sarcastic statements have significantly reduced the accuracy in automatically detecting sentiment analysis or fake news. The paper presents natural language processing based text embedding techniques utilizing the ensemble approach in this study. The framework proposes the generation of text embeddings from Word2Vec, Global vectors for word representation (GloVe), and long short-term memory (LSTM) models. Word2Vec and GloVe are word embeddings based generators, while LSTM considers full text. Thus, the proposed framework encapsulates small tweets and sentences scenarios to detect sarcasm. Various natural language processing approaches have been developed, but each has textual context and proximity restrictions. Thus, the proposed framework overcomes this restriction by using word and sentence embeddings and ensembling the output. Two social media datasets, the Headlines dataset and the Twitter dataset, are used to test the proposed model. Headlines datasets have long sentences, while Twitter datasets have short tweets. The accuracy of 81.4% and 88.9% are achieved, respectively. The accuracy metric values are better than previous state-of-art frameworks.