Sarcasm Detection of Political News on Social Media (MS THESIS)
By: Aakash Ali Sahito 17-MSIT-01 Dr.Adnan Manzoor.
Material type:
BookPublisher: QUEST Nawabshah 2023Description: 51p.Subject(s): Department of Information TechnologyOnline resources: Click here to access online Summary: ABSTRACT
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.
Keywords: Sentiment Analysis; Opinion Mining, Reviews; Text Analysis; Bag-Of- Words; Sentiment Analysis Challenges: Accuracy.
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| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Thesis and Dissertation
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Research Section | Available | MP/90-1310 |
ABSTRACT
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.
Keywords: Sentiment Analysis; Opinion Mining, Reviews; Text Analysis; Bag-Of- Words; Sentiment Analysis Challenges: Accuracy.
xi
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