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- DS-220058 type ResearchPaper assertion.
- author-list _1 0000-0002-4472-1681 assertion.
- author-list__1 _2 0000-0002-6785-9691 assertion.
- author-list__2 _3 0000-0003-0636-4000 assertion.
- author-list__3 _4 0000-0002-3340-8112 assertion.
- author-list__4 _5 0000-0002-5169-209X assertion.
- DS-220058 issue "1-2" assertion.
- DS-220058 volume "6" assertion.
- 0000-0002-4472-1681 name "Richa Sharma" assertion.
- 0000-0002-3340-8112 name "Prakher Pandey" assertion.
- 0000-0002-5169-209X name "Vishal Maurya" assertion.
- 0000-0002-6785-9691 name "Simrat Deol" assertion.
- 0000-0003-0636-4000 name "Udit Kaushish" assertion.
- 04gzb2213 name "University of Delhi, India" assertion.
- 2451-8492 title "Data Science" assertion.
- DS-220058 title "DWAEF: a deep weighted average ensemble framework harnessing novel indicators for sarcasm detection" assertion.
- DS-220058 date "2023" assertion.
- DS-220058 isPartOf 2451-8492 assertion.
- DS-220058 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." assertion.
- 0000-0002-4472-1681 email "richasharma@keshav.du.ac.in" assertion.
- 0000-0002-3340-8112 email "prakher205723@keshav.du.ac.in" assertion.
- 0000-0002-5169-209X email "vishal205750@keshav.du.ac.in" assertion.
- 0000-0002-6785-9691 email "simrat205711@keshav.du.ac.in" assertion.
- 0000-0003-0636-4000 email "udit205805@keshav.du.ac.in" assertion.
- 0000-0002-4472-1681 affiliation 04gzb2213 assertion.
- 0000-0002-3340-8112 affiliation 04gzb2213 assertion.
- 0000-0002-5169-209X affiliation 04gzb2213 assertion.
- 0000-0002-6785-9691 affiliation 04gzb2213 assertion.
- 0000-0003-0636-4000 affiliation 04gzb2213 assertion.
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