Sentiment Analysis for the Detection of Sarcastic and Ironic Tweets
Susmita Sadanand1, Govardhan Hegde K2
1Susmita Sadanand*, M. Tech Student, Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal, Karnataka, India.
2Govardhan Hegde K., Asst Professor, Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal, Karnataka, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 25, 2020. | Manuscript published on March 10, 2020. | PP: 2377-2382 | Volume-9 Issue-5, March 2020. | Retrieval Number: B6359129219/2020©BEIESP | DOI: 10.35940/ijitee.B6359.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Detection of Sarcasm and Irony, Natural Language Processing, Sentiment Analysis.
Scope of the Article: Predictive Analysis