Performance Evaluation of Word Embeddings for Sarcasm Detection- A Deep Learning Approach

Annie Johnson, Annie and Karthik, R. (2021) Performance Evaluation of Word Embeddings for Sarcasm Detection- A Deep Learning Approach. In: ICASISET 2020, 16-17 May 2020, Chennai, India.

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Sarcasm detection is a critical step to sentiment analysis, with the aim of understanding, and exploiting the available information from platforms such as social media. The accuracy of sarcasm detection depends on the nature of word embeddings. This research performs a comprehensive analysis of different word embedding techniques for effective sarcasm detection. A hybrid combination of optimizers including; the Adaptive Moment Estimation (Adam), the Adaptive Gradient Algorithm (AdaGrad) and Adadelta functions and activation functions like Rectified Linear Unit (ReLU) and Leaky ReLU have been experimented with.Different word embedding techniques including; Bag of Words (BoW), BoW with Term Frequency–Inverse Document Frequency (TF-IDF), Word2vec and Global Vectors for word representation (GloVe) are evaluated. The highest accuracy of 61.68% was achieved with theGloVeembeddings and an AdaGrad optimizer and a ReLU activation function, used in the deep learning model.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: sarcasm deep learning word embedding sentiment analysis
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: EAI Editor III.
Date Deposited: 08 Mar 2021 10:33
Last Modified: 08 Mar 2021 10:33

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