<scp>FF</scp> ‐Net: A Feature Fusion Network to Identify Sarcasm in Telugu Conversational Texts

ABSTRACT Sarcasm, a complex linguistic form combining humor and criticism, often involves conveying meanings opposite to literal meanings, posing significant challenges in sentiment analysis. This endeavor becomes complicated when it comes to resource‐poor Indian indigenous languages like Telugu, which require more adequate resources and exhibit intricate morphology. Detecting sarcasm in such contexts necessitates the creation of a well‐balanced and annotated corpus. To address this gap, the primary aim of the paper is to curate a Telugu corpus of 10,000 conversations, including 5000 sarcastic and 5000 non‐sarcastic instances, using a multi‐annotator strategy. Additionally, this paper proposes a novel feature fusion network, that is, “FF‐Net”, that integrates neural features with manually extracted handcrafted features to detect sarcasm in Telugu conversational texts. To test and validate the proposed work, extensive experimentations were done and the results demonstrate the significance of using handcrafted features for sarcasm detection, with the proposed model achieving 95.65% accuracy, outperforming existing baselines by 2.75% and surpassing state‐of‐the‐art models by 2.80%. These results underline the efficacy of fusing automated and manual feature extraction techniques, offering a robust approach to sarcasm detection with improved performance.