http://purl.org/np/RA_VrHwZaWRC92WxMymJ0wbq8-Y2gsNwr2eSNgJXSwYAo#Head http://purl.org/np/RA_VrHwZaWRC92WxMymJ0wbq8-Y2gsNwr2eSNgJXSwYAo http://www.nanopub.org/nschema#hasAssertion http://purl.org/np/RA_VrHwZaWRC92WxMymJ0wbq8-Y2gsNwr2eSNgJXSwYAo#assertion http://purl.org/np/RA_VrHwZaWRC92WxMymJ0wbq8-Y2gsNwr2eSNgJXSwYAo http://www.nanopub.org/nschema#hasProvenance http://purl.org/np/RA_VrHwZaWRC92WxMymJ0wbq8-Y2gsNwr2eSNgJXSwYAo#provenance http://purl.org/np/RA_VrHwZaWRC92WxMymJ0wbq8-Y2gsNwr2eSNgJXSwYAo http://www.nanopub.org/nschema#hasPublicationInfo http://purl.org/np/RA_VrHwZaWRC92WxMymJ0wbq8-Y2gsNwr2eSNgJXSwYAo#pubinfo http://purl.org/np/RA_VrHwZaWRC92WxMymJ0wbq8-Y2gsNwr2eSNgJXSwYAo http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication http://purl.org/np/RA_VrHwZaWRC92WxMymJ0wbq8-Y2gsNwr2eSNgJXSwYAo#assertion http://id.crossref.org/issn/2451-8492 http://purl.org/dc/terms/title Data Science https://doi.org/10.3233/DS-230058 http://purl.org/dc/terms/abstract Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers. https://doi.org/10.3233/DS-230058 http://purl.org/dc/terms/date 2023 https://doi.org/10.3233/DS-230058 http://purl.org/dc/terms/isPartOf http://id.crossref.org/issn/2451-8492 https://doi.org/10.3233/DS-230058 http://purl.org/dc/terms/title DWAEF: a deep weighted average ensemble framework harnessing novel indicators for sarcasm detection https://doi.org/10.3233/DS-230058 http://purl.org/pav/authoredBy https://orcid.org/0000-0002-3340-8112 https://doi.org/10.3233/DS-230058 http://purl.org/pav/authoredBy https://orcid.org/0000-0002-4472-1681 https://doi.org/10.3233/DS-230058 http://purl.org/pav/authoredBy https://orcid.org/0000-0002-5169-209X https://doi.org/10.3233/DS-230058 http://purl.org/pav/authoredBy 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http://xmlns.com/foaf/0.1/name Richa Sharma https://orcid.org/0000-0002-5169-209X http://schema.org/affiliation https://ror.org/04gzb2213 https://orcid.org/0000-0002-5169-209X http://schema.org/email vishal205750@keshav.du.ac.in https://orcid.org/0000-0002-5169-209X http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://xmlns.com/foaf/0.1/Person https://orcid.org/0000-0002-5169-209X http://xmlns.com/foaf/0.1/name Vishal Maurya https://orcid.org/0000-0002-6785-9691 http://schema.org/affiliation https://ror.org/04gzb2213 https://orcid.org/0000-0002-6785-9691 http://schema.org/email simrat205711@keshav.du.ac.in https://orcid.org/0000-0002-6785-9691 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://xmlns.com/foaf/0.1/Person https://orcid.org/0000-0002-6785-9691 http://xmlns.com/foaf/0.1/name Simrat Deol https://orcid.org/0000-0003-0636-4000 http://schema.org/affiliation https://ror.org/04gzb2213 https://orcid.org/0000-0003-0636-4000 http://schema.org/email udit205805@keshav.du.ac.in 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