{"id":4139,"date":"2020-11-26T08:40:14","date_gmt":"2020-11-26T13:40:14","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?p=4139"},"modified":"2025-04-15T13:53:20","modified_gmt":"2025-04-15T17:53:20","slug":"senior-capstone-2","status":"publish","type":"post","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/2020\/11\/26\/senior-capstone-2\/","title":{"rendered":"Senior Capstone"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Sarcasm Detection Using Neural Nets<\/h2>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Abstract<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">Over the last decade, researchers have come to realize that sarcasm detection is more than just another natural language task such as sentiment analysis. Problems like human error and longer processing times pertaining to sarcasm arise because previous researchers manually created features that would detect sarcasm. In an effort to limit these problems, researchers desisted from using the pre-crafted-feature-prediction models and turned to using neural networks to predict sarcasm. To understand sarcasm, one needs to have a bit of background information on the topic, common shared knowledge and also exist in the space in which the sarcastic statement exists. With this in mind, introducing visual aspects of a conversation would help improve the accuracy of a sarcasm prediction model. <\/p>\n\n\n\n<a href=\"https:\/\/drive.google.com\/file\/d\/10_CnGGLQBPoAtiUhwarFvVTq9x6iT1gN\/view?usp=sharing\">Paper<\/a><br>\n\n\n\n<a href=\"https:\/\/youtu.be\/Cnj8zgJ2rik\">Software Demo Video<\/a><br>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Software Architecture Diagram<\/strong><\/h5>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"819\" src=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2020\/11\/SarcasmStructure-3-1024x819.png\" alt=\"\" class=\"wp-image-4157\" srcset=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2020\/11\/SarcasmStructure-3-1024x819.png 1024w, https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2020\/11\/SarcasmStructure-3-300x240.png 300w, https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2020\/11\/SarcasmStructure-3-768x614.png 768w, https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2020\/11\/SarcasmStructure-3.png 1500w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Sarcasm Detection Using Neural Nets Abstract Over the last decade, researchers have come to realize that sarcasm detection is more than just another natural language task such as sentiment analysis. Problems like human error and longer processing times pertaining to sarcasm arise because previous researchers manually created features that would detect sarcasm. In an effort &#8230; <a title=\"Senior Capstone\" class=\"read-more\" href=\"https:\/\/portfolios.cs.earlham.edu\/index.php\/2020\/11\/26\/senior-capstone-2\/\" aria-label=\"Read more about Senior Capstone\">Read more<\/a><\/p>\n","protected":false},"author":92,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[101,121,19],"tags":[131,130],"class_list":["post-4139","post","type-post","status-publish","format-standard","hentry","category-101","category-joel-michael-nti-kyeremeh","category-student","tag-neural-nets","tag-sarcasm-detection"],"_links":{"self":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts\/4139","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/users\/92"}],"replies":[{"embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/comments?post=4139"}],"version-history":[{"count":18,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts\/4139\/revisions"}],"predecessor-version":[{"id":9889,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts\/4139\/revisions\/9889"}],"wp:attachment":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/media?parent=4139"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/categories?post=4139"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/tags?post=4139"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}