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As machine learning ascends the peak of computer science zeitgeist, the usageand experimentation with sentiment analysis using various forms of textual dataseems pervasive. The effect is especially pronounced in formulating securitiestrading strategies, due to a plethora of reasons including the relative ease ofimplementation and the abundance of academic research suggesting automatedsentiment analysis can be productively used in trading strategies. The sourcedata for such analyzers ranges a broad spectrum like social media feeds,micro-blogs, real-time news feeds, ex-post financial data etc. The abstracttechnique underlying these analyzers involve supervised learning of sentimentclassification where the classifier is trained on annotated source corpus, andaccuracy is measured by testing how well the classifiers generalizes on unseentest data from the corpus. Post training, and validation of fitted models, theclassifiers are used to execute trading strategies, and the correspondingreturns are compared with appropriate benchmark returns (for e.g., the S&P500returns).  In this paper, we introduce $\underline{a\ novel\ technique\ of\ using\price\ volatilities\ to\ empirically\ determine\ the\ sentiment\ in\ news\data}$, instead of the traditional reverse approach. We also perform metasentiment analysis by evaluating the efficacy of existing sentiment classifiersand the precise definition of sentiment from securities trading context. Wescrutinize the efficacy of using human-annotated sentiment classification andthe tacit assumptions that introduces subjective bias in existing financialnews sentiment classifiers.

PrivySense: $\underline{Pri}$ce $\underline{V}$olatilit$\underline{y}$  based $\underline{Sen}$timent$\underline{s}$ $\underline{E}$stimation from  Financial News using Machine Learning