# Deep Learning for Sarcasm Identification in News Headlines
## 2 Literature Review
and [PERSON] and [PERSON] combined word embeddings with convolutional neural networks (CNN), known as CNN-LSTM units, and reached state-of-the-art performance. Additionally, attentive RNNs perform well when used with Word2 Vec embeddings that have already undergone training and have contextual data. Reference [21] proposed a methodology for a news headline dataset was in a JSON file format consisting of 26,700 headlines, with sarcastic headlines (11,700) and non-sarcastic headlines (14,900) from the Orion and HuffPost, respectively. The proposed ML model is a supervised learning model consisting of headlines and a label indicating whether the headline is sarcastic or not. The first step was performed by Tokenizer of Keras, which converts initial headlines/sentences into a much more suitable form where sentences are converted into sequences of 300 words. There were 29.6 k unique words, and after removing spaces, 20 k words were left. The next step was to prepare an embedding matrix using pre-trained embeddings of words by Stanford. The shape of this matrix was [20,000, 300] which then was fed to the BLSTM by flattening all LSTM sequences and feeding it to a normal NN layer to output the label. In work of [22], the model took pre-trained user embedding as context and tweets as a dataset to output a binary value classifying the tweet as sarcastic or not sarcastic. However, the authors removed the context path because the nature of sarcasm does not dependent on the author of the news. LSTM, which is new, was implemented for encoding the context of words occurring in sentences. The forward LSTM reads the sequence of the hidden states in forward order, whereas the backward LSTM reads the given sequence in backwards or reverse order to calculate hidden words in the reverse direction. By removing context/word-embeddings, the author was not considered, and the use of embeddings were considered as a baseline. The dataset was then separated into train, validate, and test data which was done in an 80:10:10 ratio. The baseline had an accuracy of 84.88%, whereas this model has accuracy of 89.7%. This model has improved upon the baseline by ~5%. Reference [23] proposed a hybrid deep learning model for sarcasm detection in Indian indigenous languages using word-emoji embeddings. The model is a supervised learning model that uses a combination of word embeddings and emoji embeddings to detect sarcasm. The dataset used in the study was collected from social media platforms such as Twitter, Facebook, and Reddit. The authors preprocessed the data and created a corpus of 23,000 sentences. The dataset was then split into training, validation, and testing sets in the ratio of 70:15:15. The proposed model uses a hybrid architecture of CNN and LSTM for feature extraction and classification. The model achieved an accuracy of 85.68% on the test set, which outperformed the baseline model that achieved an accuracy of 75.44%. This study demonstrated the effectiveness of using a hybrid deep learning model for sarcasm detection in Indian indigenous languages. Reference [24] presented a machine learning approach to analyze the effect of hyperboles on sarcasm detection using negative sentiment tweets. The authors collected a dataset of 8000 negative sentiment tweets, out of which 2000 tweets were labeled as sarcastic using manual annotation. The authors preprocessed the data by removing stop words, stemming,and tokenizing the tweets. They then used a combination of traditional machine learning algorithms such as support vector machines (SVM), naive Bayes, and random forest to classify the tweets as sarcastic or not sarcastic. The authors also introduced a hyperbole score metric to capture the degree of hyperbole present in the tweets. The experimental results showed that SVM outperformed the other classifiers with an accuracy of 85.8%. The authors found that hyperbole had a significant impact on sarcasm detection, and their study provides valuable insights for the development of more accurate sarcasm detection models.