Abstract
Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not really significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features are classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the performance of each feature set are also shown as additional information.
Highlights
The magnitude of data generated through social media today is colossal
Spotting and handling them correctly is crucial in an automated Natural Language Processing (NLP) systems, mainly since sarcasm can flip the polarity of a sentence [5], [6]
A recent study [24] used a deep learning Bidirectional Encoder Representations from Transformers (BERT) architecture to experiment on the idea of using historical information to detect sarcasm
Summary
The magnitude of data generated through social media today is colossal. They are good for data analysis since they are very personal [1]. Sarcasm is defined as a positive utterance or sentence with underlying negative intention [4] It is regarded as one of the most challenging issues in the Natural Language Processing (NLP) field [5]. As a counter-measure for this short-coming, writers of tweets tend to leave contextual clues for sarcasm in creative ways such as hashtags and hyperboles [4], [7]. This kind of clues is what this work is trying to find and exploit
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
