• Shreyas Devarkar
• Karan Darji
• Ronit Bhatt
• Hezal Lopes

- The growth of social media has been exceptional within the recent years. Vast quantity of knowledge is being place out on to the general public domain through social media. Twitter may be amongst the social giants that is a medium for showcasing information to the general public. Sentimental Analysis is that the best thanks to decide people’s opinion concerning a selected post. The planned work presents analysis for sentimental behavior and wittiness detection in Twitter dataset. The planned work utilizes the support vector machine (SVM) to classify information into sarcastic and non-sarcastic and conjointly to classify the dataset into positive, negative or neural behavior. However, our major objective is to extend accuracy of finding of sarcasm or irony detection in tweets that is troublesome to detect as there's no face expression or intonation concerned in written information that eventually hampers the performance of sentiment analysis because the meant context of the text is scan in an exceedingly totally different manner by the machine in sarcastic or ironical texts.

Sentiment analysis , sarcastic , non-sarcastic , machine learning , social media , twitter , SVM , classification.

"Sarcasm Detection & Sentimental analysis of Tweets Using Support Vector Machine.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.65-70, April-2019, Available :http://www.jetir.org/papers/JETIR1904107.pdf

"Sarcasm Detection & Sentimental analysis of Tweets Using Support Vector Machine.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp65-70, April-2019, Available at : http://www.jetir.org/papers/JETIR1904107.pdf