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Political alignment identification is an author profiling task that aims at identifying political bias/orientation in people’ writings. As usual in any automatic text analysis, a critical aspect here is having available adequate data sets so that the data mining and machine learning approaches can obtain reliable and informative results. This article makes a contribution in this regard by presenting a new corpus for the study of political alignment in documents of Argentinian journalists. The study also includes several kinds of analysis of documents of pro-government and opposition journalists such as the relevance of terms in each journalist class, sentiment analysis, topic modelling and the analysis of psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count (LIWC) system. From the experimental results, interesting patterns could be observed such as the topics both types of journalists write about, how the sentiment polarities are distributed and how the writings of pro-government and opposition journalists differ in the distinct LIWC categories., Facultad de Informática

The challenging problem of computational bioimage analysis receives growing attention from life sciences. Fluorescence microscopy is capable of simultaneously visualizing multiple molecules by staining with different fluorescent dyes. In the analysis of the result multichannel images, segmentation of ROIs resembles only a first step which must be followed by a second step towards the analysis of the ROI's signals in the different channels. In this paper we present a system that combines image segmentation and information visualization principles for an integrated analysis of fluorescence micrographs of tissue samples. The analysis aims at the detection and annotation of cells of the Islets of Langerhans and the whole pancreas, which is of great importance in diabetes studies and in the search for new anti-diabetes treatments. The system operates with two modules. The automatic annotation module applies supervised machine learning for cell detection and segmentation. The second information visualization module can be used for an interactive classification and visualization of cell types following the link-and-brush principle for filtering. We can compare the results obtained with our system with results obtained manually by an expert, who evaluated a set of example images three times to account for his intra-observer variance. The comparison shows that using our system the images can be evaluated with high accuracy which allows a considerable speed up of the time-consuming evaluation process. (C) 2009 Elsevier Ltd. All rights reserved.