SOCIAL NETWORK SARCASTIC DATA ON CONTEXTUAL TEXT INVOLVING IN SENTIMENT ANALYSIS ON TWITTER CORPUS OF BIG DATA APPLICATION

Abstract
Information retrieval in context based has become one of the impecuniousness in the lively information trends. Commonly information retrieval on context based is slowly important on sarcastic functions and now days should be very tough to data heterogeneity and not easy on modes of data circulate function. This paper presents a context based information retrieval sarcastic system to perform real time retrieval of data. This model operates on the semantic comparative of the data, rather than the content similarity. Hence this technique exhibits better and efficient retrieval levels providing high adequate approach. The retrieved data’s are pre-processed and feature vectors are created from the small instance of ranking methods. Polarity matching is used to filter sentimentally correlated results and result based output ranking is performed to perform further elimination of proper circumstances of the results. Experiments conducted using the proposed model exhibits very high true retrieval rates, along with high precision and recall levels exhibiting the competence of the proposed work on the sarcastic text information retrieval is considered to be effective.

Authors
N Karthikeyan
Srimad Andavan Arts and Science College, India

Keywords
Context Based Data, Regression, Polarity, Semantic, Contextualization
Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 1 , Issue: 1 )
Date of Publication :
December 2019

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