Open AccessSentiment Analysis for the Detection of Sarcastic and Ironic Tweets
Author(s) -
Susmita Sadanand,
Govardhan Hegde
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b6359.039520
Subject(s) - sarcasm , sentiment analysis , social media , computer science , irony , affect (linguistics) , artificial intelligence , filter (signal processing) , feature (linguistics) , natural language processing , order (exchange) , psychology , world wide web , linguistics , communication , philosophy , computer vision , finance , economics
This paper aims at detecting sarcasm and irony tweets based on the application of natural language processing and sentiment analysis. These days twitter has become most widely used social media. Most of the tweets generated affect people’s mental health and thought process. Even though many tweets have a positive effect a few of them are targeted towards people for bullying and hurting them. So it is necessary that we filter the tweets and identify the negative ones so that people may have a positive experience on this platform. In order to do this, this paper provides a methodology that helps in analyzing the sentiments behind the tweets and classify them into positive and negative tweets. Neural Network is used to achieve this. Feature engineering is applied on the dataset and then using Neural Network we try to get the result.