Research pick: Yeah, right! - "CASD on enhancing sentiment analysis using context-aware sarcasm detection on social media"

A new approach to sentiment analysis could reduce one of the field’s most stubborn sources of error: the misinterpretation of sarcasm. The system is discussed in the International Journal of Intelligent Engineering Informatics. It shows that machine-learning models can be trained to recognise when language means the opposite of what it appears to say. This is an important advance in language processing with implications for businesses, policymakers, and analysts who rely on automated readings of public opinion.

Sentiment analysis aims to classify text as positive, negative or neutral, but often comes unstuck when analysing remarks that a human reader would immediately recognise as sarcasm. Sarcastic remarks often invert the literal meaning, and so conventional algorithms can misread the tone, distorting everything from political polling to consumer-behaviour forecasts. With online communication proliferating across social media and forums, the cost to society of such errors is on the increase.

The new framework system combines two distinct techniques to handle this problem. First, it uses BERT, Bidirectional Encoder Representations from Transformers. This is a language model that reads text in two ways and can identify subtle cues that signal irony, a contradiction or a tonal shift. These contextual embeddings are essentially numerical representations of meaning. They are passed to a so-called random forest algorithm for classification to improve reliability. Random forests are well suited to spotting complex, non-linear patterns in data, making them a natural complement to BERT’s linguistic sensitivity.

The researchers trained their new system on a bespoke dataset rich in realistic, fine-grained examples of sarcastic speech. When they tested the trained model against established sentiment-analysis models, including lexicon-based, statistical, and deep learning systems, they were able to spot sarcasm with 85 per cent accuracy. The same system could also identify neutral sentiment, which is an area where existing tools often struggle because sarcasm can the true intent of what is being said.

The researchers emphasise how more accurate detection of sarcasm could yield more trustworthy analytics across sectors that depend on understanding public mood.

Davidson, G.P., Ravindran, D. and Pratheeba, R.A. (2025) ‘CASD on enhancing sentiment analysis using context-aware sarcasm detection on social media’, Int. J. Intelligent Engineering Informatics, Vol. 13, No. 3, pp.267–296.