# Deep Learning for Sarcasm Identification in News Headlines
## 2 Literature Review
Reference [25] proposed an intelligent machine learning-based sarcasm detection and classification model for social networks. The model uses a supervised learning approach to detect and classify sarcastic tweets. The dataset used in the study was collected from Twitter, and it consisted of 10,000 tweets with an equal distribution of sarcastic and non-sarcastic tweets. The authors preprocessed the data and used feature extraction techniques to create a set of relevant features. The model used in this study is based on ensemble learning, which combines the predictions of multiple models to improve accuracy. The study evaluated the model's performance on various classification algorithms such as decision tree, random forest, naive Bayes, and support vector machine. The model achieved an accuracy of 92.14% using the random forest algorithm, which outperformed the other classifiers. This study demonstrates the effectiveness of ensemble learning techniques for detecting sarcasm and classification on social networks. Reference [26] proposed a deep learning-based model for sarcasm detection in news headlines. The authors used a dataset of 26,700 headlines, including 11,700 sarcastic and 14,900 non-sarcastic headlines collected from the Orion and HuffPost. The dataset was in a JSON file format. This supervised learning model uses a Bidirectional Long Short-Term Memory (BLSTM) network and a convolutional neural network (CNN) for feature extraction and classification. The authors used the Keras Tokenizer to convert the headlines into sequences of 300 words to prepare the embedding matrix. The embedding matrix used pre-trained embeddings of words by Stanford, and its shape was [20,000, 300]. The authors evaluated the performance of the proposed model and reported an accuracy of 90.19%. They compared the performance of the proposed model with the state-of-the-art models and reported that their model outperformed others in terms of accuracy and computational time. This study demonstrates the effectiveness of using a deep learning-based model for sarcasm detection in news headlines. Reference [27] proposed a multilingual approach to fake news detection that takes into account the presence of satire. The authors use a dataset of news articles in multiple languages and developed a machine learning model to classify them as real or fake. They also trained the model to identify satrical elements in the articles. The model is based on a convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) network and was trained using a combination of word embeddings and character-level embeddings. The results of the experiments show that the proposed method outperforms several state-of-the-art methods in fake news detection, achieving an accuracy of over 90% on some datasets. The authors also show that their method is effective in detecting satrical elements in news articles, which is a significant contribution to the field. Reference [28] proposed a deep multi-task learning approach to tackle both tasks simultaneously. The authors used a dataset of tweets to develop a model that combines a convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) network. The model was trained using a multi-task learning framework that jointly optimizes both sentiment analysis and sarcasm detection. The authors also experimented with different ways of combining the two tasks, including using a shared encoder or separate encoders. The results of these experiments show that the proposed method outperforms several state-of-the-art methods in both sentiment analysis and sarcasm detection. The authors also show that the multi-task learning approach is effective in leveraging the shared information between the two tasks, resulting in better performance than training the tasks separately. Reference [29] presents a systematic review of machine learning techniques for stance detection and their applications.