# Deep Learning for Sarcasm Identification in News Headlines
## 2 Literature Review  
Previous research on sarcasm detection mainly focused on utilizing Twitter datasets, but more advanced techniques have since been developed, including obtaining written news headlines from formal journalists and utilizing headlines exclusively containing sarcastic content from The Onion. Currently, researchers are utilizing convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) neural networks as methodologies in natural language processing (NLP) [4]. [PERSON] [5] proposed the use of a multi-channel attention-based Bidirectional Long Short-Term Memory (BLSTM) technique for detecting sarcasm in news headlines. In addition, some researchers have applied the naive Bayes classifier and support vector machines to analyze data by identifying sarcasm. The accuracy of existing solutions, which were based on features such as punctuation, phrase structure, and hashtags, was reported to be over 75% when applied to datasets from various social media networks. The latest research has adopted deep learning techniques, which have proven to be superior to shallow modeling techniques in detecting sarcasm in text [6]. Most researchers currently prefer deep learning methods over manual feature extraction methods [5]. Previously, research has primarily focused on the use of tweets as input data for sarcasm detection. To distinguish sarcasm, contextual clues such as hashtags are often used as indicators, with keywords such as \"#sarcasm\", \"#sarcastic\", \"#notsarcasm\", and \"#not\" believed to provide the best indication of a sarcastic tweet [3; 7; 8]. In previous studies, researchers have extracted feature sets from Twitter datasets consisting of 9400 tweets, with 5800 expressing a positive sentiment, 3100 expressing a negative sentiment, and 500 expressing a neutral sentiment [9]. Convolutional neural networks (CNNs) and long-term and short-term memory (LSTM) neural networks are two commonly used methodologies for natural language processing (NLP) in sarcasm detection [10]. A multi-channel attention-based mechanism using the Bidirectional Long Short-Term Memory (BLSTM) technique has also been proposed for detecting sarcasm in news headlines [11]. Researchers have also added additional rules based on patterns of sarcasm, such as counting positive/negative words and emotional words, as well as collecting tweets that included recurring words such as \"sarcasm\", \"sarcastic\", \"being sarcastic\", \"yay!\", and \"nay!\" [12]. [PERSON] and [PERSON] [13] state that the twitter has been a very big network in terms of active users and tweets made per day [14]. The limitations of tweet analysis for sarcasm detection include the 140-character limit of tweets, which can make it difficult to understand the intended meaning and even more challenging to detect sarcasm [15; 16]. Inprevious studies, tweets have been analyzed and classified according to mood, predefined sets (such as events, opinion, transactions, and private messages), and non-contextual characteristics such as slang and sentiments expressed by words [17]. Most of the previous work on sarcasm detection in tweets was based on the polarity of the users' sentiments, with a focus on the content of the tweets. [PERSON] and [PERSON] [18] proposed a set of attributes, including profanity, use of slang, and their \"semantic action\", and used an SVM classifier to classify humorous tweets [19]. Researchers rely on hashtags to classify the tone of a tweet. Researchers have used public modeling [20] to detect the original tone of tweets by classifying the variety of the tone as well as the manifestation on Twitter. This highlights the importance of historical data gathered from a user's past tweets, although this approach is effective only if a historical record of a user exists. RNNs and other DL neural networks that use pre-trained word embeddings to tackle FL have been successful because of word embeddings, or trained maps of words to real-valued vectors. In fact, [PERSON] et al.