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

Artificial Intelligence;Machine Learning;Logic in Computer Science