• Jasandeep Kaur
• Dr. Rajeev Kumar

The text analytics are being used in a number of applications ranging from trend detection to sentiment analysis. These models are applied over the different kinds of data, which involves the limited length text responses from micro-blogging portals (e.g. Twitter) and product reviews. Most of the text analytics services are used to determine the public sentiment for the target products, political issues, news, etc. But the major problem in the case of sentiment analysis is caused by the sarcastic content, which depicts the different context, but caries a different meaning. Hence, it’s very important to accurately detect the sarcastic tweets is increasing every year and decreasing the accuracy of the existing text analytical engines. This paper presents a survey on phases for sarcasm detection and also discusses various approaches based upon the combination of multiple features for classifying the text. This paper targets to improve the overall sarcasm detection accuracy by using the maximum possible feature combinations.

Sarcasm detection, Support Vector Machine, Twitter analytics, Maximum Entropy

"Text Analytical Models for Data Collected from Micro-blogging Portal – A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.81-86, July-2018, Available :http://www.jetir.org/papers/JETIR1807361.pdf

"Text Analytical Models for Data Collected from Micro-blogging Portal – A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp81-86, July-2018, Available at : http://www.jetir.org/papers/JETIR1807361.pdf