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The predilection and efficacy of the web has procreated significant and magnificent data in the arena of online social networks, impacting criticality of Social Data Analytics profusely. This inclination of expressing opinions, attributed by its diversity, produces voluminous online information that is questionable in terms of its veracity. As different individuals attempt to spread gossipy tidbits, generally only for the sake of entertainment and/or spreading rumors, it is required that there exist strategies utilizing which individuals can, without much hassle, check data believability of information posted on online networks. The recent research endeavors done have been essentially centered around following and displaying opinions of individuals. This paper proposes approach for mechanizing the procedure of congregating opinions posted on Twitter, probing the validity of these beliefs, and assessing its credibility, by implementing the strategies for Sentiment Analysis and Information Credibility Analysis. This implicates assimilation of Latent Dirichlet Allocation (LDA) calculation for grouping of subjects inside the tweets and semi-directed Support Vector Machine (SVM) for sentiments’ investigation. As a last point, dominant part choice, by looking at the quantity of Contrastive Opinions around a point is performed, for Credibility Analysis. To address the purpose, an innovative tool, to automate information credibility analysis has been developed, named as E-OPTIMA (EnhancedOPinionated Tweet Implied Mining and Analysis) ver. 2.0.0. which is an enhanced version of the tool OPTIMA ver. 1.0.0. that caters to automation of sentiment analysis and the results have been presented in a graphical form and tabular manner for ease of understanding.

E-optima(enhanced-opinionated tweet implied mining and analysis) an innovative tool to automate information credibility analysis