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Continuous social text streams, such as tweets, provide a timeline of discussions. Topic modeling techniques such as Latent Dirichlet Allocation (LDA) have been used to extract the topics being discussed on social media streams. Recently, Online LDA has been proposed as a fast alternative for topic extraction, based on on-line stochastic optimization, while sentiment analysis is often used to track the polarity of posts. In this paper, we propose an online technique, integrating Online LDA and sentiment analysis to extract more refined polarity-aware topics within an online learning framework from continuous Twitter streams.

Abstract-The magnetic resonance imaging (MRI) is a diagnostic and treatment evaluation tool which is very widely used in various areas of medicine. MRI images provide very high quality images of the brain tissue and so can be used to study the brain conditions. This research paper proposes a productive technique to classify brain MRI images. Examining the MRI brain images manually is not only slow but is also error prone. In order to both speed up the process and maintain the quality of the classification we need a very high-quality classification system. In this research work, advanced classification techniques based on the well known SIFT and Gabor features are applied on brain images. From our analysis we observed that a hybrid feature derived with SIFT and Gabor features yielded a higher accuracy than Gabor features alone.

In this paper, we describe our solution to the RecSys2014 challenge and results on the test set. We briefly describe some of the challenges, then describe the methodology which starts with feature extraction and construction using the provided tweet data, in combination with IMDB as an external source. Feature construction also involved computing similarity values in a latent factor space to deal with the sparsity and lack of semantics of text-based and other nominal features. We also describe our machine learning models which consist of several stages, including a classifier, followed by a Learning to Rank (LTR) model, with a repairing mechanism to further correct minority class (non-zero engagement) predictions that are close to the boundary. Finally, we draw conclusions in the form of lessons learned and future work toward improving our results.