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The opinion expressed in various Web sites and social-media is an essentialcontributor to the decision making process of several organizations. Existingsentiment analysis tools aim to extract the polarity (i.e., positive, negative,neutral) from these opinionated contents. Despite the advance of the researchin the field, sentiment analysis tools give \textit{inconsistent} polarities,which is harmful to business decisions. In this paper, we propose SentiQ, anunsupervised Markov logic Network-based approach that injects the semanticdimension in the tools through rules. It allows to detect and solveinconsistencies and then improves the overall accuracy of the tools.Preliminary experimental results demonstrate the usefulness of SentiQ.

SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality