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This work proposes an unsupervised Joint Topic Viewpoint model (JTV) with the objective to further improve the quality of opinion mining in contentious text. The conceived JTV is designed to learn the hidden features of arguing expressions. The learning task is geared towards the automatic detection and clustering of these expressions according to the latent topics they confer and the embedded viewpoints they voice. Experiments are conducted on three types of contentious documents: polls, online debates and editorials. Qualitative and quantitative evaluations of the models output confirm the ability of JTV in handling different types of contentious issues. Moreover, analysis of the preliminary experimental results shows the ability of the proposed model to automatically and accurately detect recurrent patterns of arguing expressions.