Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2269
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dc.contributor.authorPal, Moumita-
dc.contributor.authorPrasad, Rajesh-
dc.date.accessioned2023-12-09T08:56:03Z-
dc.date.available2023-12-09T08:56:03Z-
dc.date.issued2023-
dc.identifier.citationpp. 1-7en_US
dc.identifier.isbn9798350333817-
dc.identifier.urihttps://doi.org/10.1109/AICAPS57044.2023.10074510-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2269-
dc.description.abstractAs www data grows, so do opinions, views, visitors, news, and comments. Using opinions, perspectives, and remarks, Natural Language Processing (NLP) professionals may classify emotions. Classifying and evaluating Bengali text emotions is becoming significant in e-commerce, journalism, movies, OTT, and security applications. The lack of Bengali corpus makes developing a Sentiment Analysis system difficult. Sarcasm is another popular social media trend. Positive words are often used to indicate hatred. Thus, it's hard to tell what these sentences mean. This study presents a method for identifying and analysing sarcasm. GloVe is used to represent words while LSTM is trained and tested on the represented characteristics. Experiments show 91.94% accuracy. Predicted sarcastic sentences are labelled as negative and added to Sentiment Analysis corpora (SA). Logistic Regression (LR), K-Nearest Neighbor (K-NN), Linear Support Vector Machine (SVM), and Random Forest (RF) are used to feature matrices for sentiment analysis. For Unigram, Bi-gram, and Tri-gram models, Linear SVM has the highest precision (92.5%), whereas LR model approaches greater accuracy (72.04%) and F1-score (68.15%). © 2023 IEEE.en_US
dc.language.isoenen_US
dc.publisher2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023en_US
dc.subjectLong short-term memoryen_US
dc.subjectMachine Learningen_US
dc.subjectNatural Language Processingen_US
dc.subjectSarcasm Detectionen_US
dc.subjectSentiment Analysisen_US
dc.titleSarcasm Detection Followed By Sentiment Analysis For Bengali Language: Neural Network & Supervised Approachen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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