| dc.contributor.author |
Sharma, Yashvardhan |
|
| dc.date.accessioned |
2023-01-02T11:09:06Z |
|
| dc.date.available |
2023-01-02T11:09:06Z |
|
| dc.date.issued |
2019 |
|
| dc.identifier.uri |
https://ieeexplore.ieee.org/document/8776609 |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8229 |
|
| dc.description.abstract |
This paper deals with the impediment of identifying sarcasm in social media text which can be used to improve sentiment analysis technique. After thorough analysis, some features were identified which could help in recognition of sarcasm. In state of art, features have been extracted from the data set which embraced standalone sentences. Proposed algorithm analyzes the impact of these features and a combination of them on the review data set in which reviews had three or more sentences, so that context of sentence is also taken into consideration by the machine before classifying a review. |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
IEEE |
en_US |
| dc.subject |
Computer Science |
en_US |
| dc.subject |
Sarcasm Detection |
en_US |
| dc.subject |
Sentiment Analysis |
en_US |
| dc.subject |
Machine Learning |
en_US |
| dc.subject |
Natural Language Processing |
en_US |
| dc.title |
FAID: Feature Aftermath for Irony Discernment |
en_US |
| dc.type |
Article |
en_US |