Sarcasm detection in dialogues has been gaining popularity among natural language processing (NLP) researchers with the increased use of conversational threads on social media. Capturing the knowledge of the domain of discourse, context propagation during dialogue, and the situational context and tone of the speaker are some important features to train machine learning models for detecting sarcasm in real time.

Researchers are building AI-driven Sarcasm detectors. According to research led by Akshi Kumar, Graduate Program on Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza, CE, Brazil, “as situational comedies vibrantly represent human mannerism and behaviour in everyday real-life situations, this research demonstrates the use of an ensemble supervised learning algorithm to detect sarcasm in the benchmark dialogue dataset, MUStARD”.

Studying dialogues

Sarcasm detection has attracted growing interest over the past decade as it facilitates accurate analytics in online comments and reviews. As a figurative literary device, sarcasm uses words in a way that deviates from the conventional order and meaning, thereby misleading polarity classification results. 

The researchers used a dialogue dataset from sitcoms for the study. Using dialogue datasets from sitcoms can invariably relate to any real-life utterance, making this work relevant for various sentiment analysis-based market and business intelligence applications for assessing insights from conversational threads on social media. 

Sarcasm is one of the key NLP challenges to sentiment analysis accuracy. Context incongruity can be used to detect sarcasm in conversational threads and dialogues where the chronological statements formulate the context of the target utterance. The researchers used an ensemble learning method to detect sarcasm in the benchmark sitcom dialogue dataset. Results establish the influence of using context with the punch-line utterance as features to train XGBoost. Further, the predictions given by the black-box XGBoost are explained using LIME and SHAP for local interpretations. 

Auditory cues such as the tone of the speaker and other acoustic markers such as voice pitch, frequency, empathetic stress and pauses, and visual cues for facial expressions that can assist sarcasm detection in audio-visual modalities need further investigation.

Other studies

Researchers at the University of Groningen’s speech technology lab in the Netherlands have recently achieved a notable milestone when they developed an AI-driven sarcastic detector. Matt Cole, a key figure in their research team, emphasised the importance of this development. He remarked that they were able to recognise sarcasm reliably, and we’re eager to grow that. The implications of this technology extend far beyond mere linguistic analysis; They have the potential to revolutionise human-AI interaction.

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