Automatic Sarcasm Detection: A Survey <s> Deep Learning-Based Approaches <s> We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for models to capitalize on contextual features, beyond lexical and syntactic cues present in utterances. For example, different speakers will tend to employ sarcasm regarding different subjects and, thus, sarcasm detection models ought to encode such speaker information. Current methods have achieved this by way of laborious feature engineering. By contrast, we propose to automatically learn and then exploit user embeddings, to be used in concert with lexical signals to recognize sarcasm. Our approach does not require elaborate feature engineering (and concomitant data scraping); fitting user embeddings requires only the text from their previous posts. The experimental results show that our model outperforms a state-of-the-art approach leveraging an extensive set of carefully crafted features. <s> BIB001 </s> Automatic Sarcasm Detection: A Survey <s> Deep Learning-Based Approaches <s> Precise semantic representation of a sentence and definitive information extraction are key steps in the accurate processing of sentence meaning, especially for figurative phenomena such as sarcasm, Irony, and metaphor cause literal meanings to be discounted and secondary or extended meanings to be intentionally profiled. Semantic modelling faces a new challenge in social media, because grammatical inaccuracy is commonplace yet many previous state-of-the-art methods exploit grammatical structure. For sarcasm detection over social media content, researchers so far have counted on Bag-of-Words(BOW), N-grams etc. In this paper, we propose a neural network semantic model for the task of sarcasm detection. We also review semantic modelling using Support Vector Machine (SVM) that employs constituency parsetrees fed and labeled with syntactic and semantic information. The proposed neural network model composed of Convolution Neural Network(CNN) and followed by a Long short term memory (LSTM) network and finally a Deep neural network(DNN). The proposed model outperforms state-of-the-art textbased methods for sarcasm detection, yielding an F-score of .92. <s> BIB002 </s> Automatic Sarcasm Detection: A Survey <s> Deep Learning-Based Approaches <s> Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an "apparently positive" sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network's baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase. <s> BIB003
As architectures based on deep learning techniques gain popularity in Natural Language Programming (NLP) applications, a few such approaches have been reported for automatic sarcasm detection as well. Reference  uses similarity between word embeddings as features for sarcasm detection. They augment these word embedding-based features with features from four prior works. The inclusion of past features is key, because they observe that using the new features alone does not suffice for good performance. Reference BIB001 presents a novel Convolutional Networkbased architecture that learns user embeddings in addition to utterance-based embeddings. The authors state that it allows them to learn user-specific context. Reference BIB002 uses a combination of a Convolutional Neural Network, a Recurrent Neural Network (Long Short-Term Memory) followed by a Deep Neural Network. They compare their approach against recursive SVM and show an improvement for the deep learning architecture. Reference BIB003 investigates the use of Deep Convolutional Networks for sarcasm detection.