A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
This work addresses sarcasm detection for natural language processing tasks like sentiment analysis, but it is incremental as it builds on existing deep learning methods with feature enhancements.
The authors tackled sarcasm detection in tweets by developing models that combine sentiment, emotion, and personality features extracted from a pre-trained convolutional neural network, achieving state-of-the-art performance on benchmark datasets.
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.