Sarcasm Detection of Political News on Social Media (MS THESIS) (Record no. 68385)

MARC details
000 -LEADER
fixed length control field 01470nam a22001337a 4500
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Aakash Ali Sahito
-- 17-MSIT-01
-- Dr.Adnan Manzoor
245 ## - TITLE STATEMENT
Title Sarcasm Detection of Political News on Social Media (MS THESIS)
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication QUEST
Name of publisher Nawabshah
Year of publication 2023
300 ## - PHYSICAL DESCRIPTION
Number of Pages 51p.
520 ## - SUMMARY, ETC.
Summary, etc ABSTRACT<br/><br/>Sarcasm is an omnipresent fact of language in documents that are prosent on internet that express negative remarks in positive way. Finding sarcasm is of hugs significanes and helpful to many applications of Natural Language Processing, such as analysis of sentiment, opinion mining and product reviews. On-going studies consider detection of automatic sarcasm as a simple text classification issue. The study does not use explicit features for detection of sarcasm and ignore the difference between sarcastic and nom sarcastic samples in real applications. In this thesis, we first explore the individuality of existing approaches to detect sarcasm in written text. Then, we propose an approach with best rate of classification and less rate of misclassification of sarcasm. We evaluate different methods like SVM, LDA, KNN & RF and get an outcome of better method in this regard with less ratio of false-positives outputs.<br/><br/>Keywords: Sentiment Analysis; Opinion Mining, Reviews; Text Analysis; Bag-Of- Words; Sentiment Analysis Challenges: Accuracy.<br/><br/>xi
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Department of Information Technology
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://tinyurl.com/ynbd77tx
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis and Dissertation
Holdings
Withdrawn status Lost status Home library Current library Date acquired Accession Number Koha item type
    Research Section Research Section 26/07/2024 MP/90-1310 Thesis and Dissertation