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.



Raheleh Makki, Eder Carvalho, Axel J. Soto, Stephen Brooks, Maria Cristina Ferreira De Oliveira, Evangelos Milios, and Rosane Minghim. 2018. ATR-Vis: Visual and Interactive Information Retrieval for Parliamentary Discussions in Twitter. ACM Transactions on Knowledge Discovery from Data, 12, 1, Article 3 (February 2018), 33 pages. (PDF)

Raheleh Makki, Axel J. Soto, Stephen Brooks and Evangelos E. Milios. Twitter Message Recommendation Based on User Interest Profiles. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 18-21, Calgary, Alberta, 2016. (PDF)

Raheleh Makki, Axel J. Soto, Stephen Brooks and Evangelos E. Milios. Active Information Retrieval for Linking Twitter Posts with Political Debates. 14th IEEE Conference on Machine Learning and Applications, pp. 238-245, Miami, December, 2015. (PDF)

Raheleh Makki, Evangelos E. Milios, and Stephen Brooks. Context-Specific Sentiment Lexicon Expansion via Minimal User Interaction. Information Visualization Theory and Applications, pp. 178-186, Lisbon, Portugal, 2014. (PDF)

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