A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks

By Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Prateek Vij. 2016

389 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “This fact was also observed in [52], where authors performed sarcasm detection in Twitter texts using a CNN network.”
CARER: Contextualized Affect Representations for Emotion Recognition (2018, background): “Other advantages of the graph approach are that minimum domain knowledge and manual effort are required to capture important contextual and latent information, which are useful to disam-biguate meaning in emotional expressions.”
SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings (2018, background): “Such word vectors include more affective clues than regular word vectors and produce better results for tasks such as emotion recognition (Poria et al. 2017), sarcasm detection (Poria et al. 2016) and aspect extraction (Poria, Cambria, and Gelbukh 2016).”
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM (2018, background): “2017), sarcasm detection (Poria et al. 2016), and aspect extraction.”
Context-Dependent Sentiment Analysis in User-Generated Videos (2017, methodology): “While the use of knowledge bases was initially more popular for the identiﬁcation of polarity in text (Cambria et al., 2016; Poria et al., 2016c), sentiment analysis researchers have recently been using statistics-based approaches, with a special focus on supervised statistical meth-ods (Socher et…”
Recent trends in deep learning based personality detection (2019, methodology): “Typically, Latent Dirichlet Allocation (LDA) (Blei et al. 2003) and its sentiment extensions (Poria et al. 2016) are used for forming clusters of semantically related words.”
Bayesian network based extreme learning machine for subjectivity detection (2017, background): “While most works approach it as a simple categorization problem, sentiment analysis is actually a ‘suitcase’ research problem that requires tackling many natural language processing (NLP) sub-tasks, including aspect extraction [4], named entity recognition [5], word polarity disambiguation [6], sema”
Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, background): “This fact was also observed in [51], where authors performed sarcasm detection in Twitter texts using a CNN network.”
Natural language based financial forecasting: a survey (2017, background): “Sentiment analysis [16] is a ‘suitcase research problem’ [19] that requires tackling many NLP sub-tasks, including aspect extraction [92], subjectivity detection [27], named entity recognition [73], and sarcasm detection [93], but also complementary tasks such as personality recognition [74], user p”
Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis (2016, methodology): “Recent work on text modality has used CNN for sentimentrelated tasks such as sarcasm detection [13] and aspect-based opinion mining [14].”
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (2018, methodology): “To get our sentence representation, we use a simple CNN with one convolutional layer followed by max-pooling (Kim, 2014; Poria et al., 2016).”
Deep learning for sentiment analysis: A survey (2018, background): “Poria et al.(117) developed a CNN-based model for sarcasm detection (sarcastic or non-sarcastic tweets classification), by jointly modelling pre-trained emotion, sentiment and personality features, along with the textual information in a tweet.”
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) (2019, methodology): “…speaker’s background and behavior in online platforms (Rajadesingan et al., 2015), embeddings of expressed sentiment and speaker’s personality traits (Poria et al., 2016), learning of user-speciﬁc representations (Wallace et al., 2016; Kolchinski and Potts, 2018), user-community features (Wallace…”
Ensemble application of convolutional and recurrent neural networks for multi-label text categorization (2017, background): “Recently, several deep learning works have applied neural networks, such as CNN and RNN, to various NLP tasks, including multi-task learning [7], sentence classification [8], multimodal analysis [22], semantic relevance detection [10], [23], polarity detection [24], machine translation [25], sarcasm”