Research Article
Review Paper: Sarcasm Detection and Observing User Behavioral
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 166 - Issue 9 |
| Published: May 2017 |
| Authors: Pooja Deshmukh, Sarika Solanke |
10.5120/ijca2017914119
|
Pooja Deshmukh, Sarika Solanke . Review Paper: Sarcasm Detection and Observing User Behavioral. International Journal of Computer Applications. 166, 9 (May 2017), 39-41. DOI=10.5120/ijca2017914119
@article{ 10.5120/ijca2017914119,
author = { Pooja Deshmukh,Sarika Solanke },
title = { Review Paper: Sarcasm Detection and Observing User Behavioral },
journal = { International Journal of Computer Applications },
year = { 2017 },
volume = { 166 },
number = { 9 },
pages = { 39-41 },
doi = { 10.5120/ijca2017914119 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2017
%A Pooja Deshmukh
%A Sarika Solanke
%T Review Paper: Sarcasm Detection and Observing User Behavioral%T
%J International Journal of Computer Applications
%V 166
%N 9
%P 39-41
%R 10.5120/ijca2017914119
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Sarcasm is a sort of sentiment where public expresses their negative emotions using positive word within the text. It is very tough for humans to acknowledge. In this way we show the interest in sarcasm detection of social media text, particularly in tweets. In this paper we study new method pattern based approach for sarcasm detection, and also used behavioral modelling approach for effective sarcasm detection by analyzing the content of tweets however by conjoint exploiting the activity traits of users derived from their past activities. By using the various classifiers such as Random Forest, Support Vector Machine (SVM), k Nearest Neighbors (k-NN) and Maximum Entropy, we check the accuracy and performance.
References
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