User generated content is different from traditional documents in structure, length, and semantics. Consequently, applying traditional natural language processing and text mining methods to emerging and challenging text mining problems does not always achieve satisfactory results. This thesis studies the impact of actively involving the user in the analytical process of such data on overcoming related challenges and improving the quality of the analysis. We investigate whether employing active learning and visualization techniques increases the benefits gained from incorporating user knowledge, and whether these techniques enhance user involvement. Moreover, our ultimate objective is to assist users to better understand the data and make decisions.