LIWC is a text analysis program that categorizes words into gram- matical and psychologically derived categories. The currently available LIWC lexicon for Brazilian Portuguese (LIWC 2007pt) is based on the 2007 version of LIWC program. As several studies indicated, LIWC 2007pt shows perfor- mance and categorization problems. In this scenario, this work highlights a new Brazilian Portuguese LIWC lexicon (LIWC 2015pt), based on LIWC 2015 program. This work compares the performance of LIWC 2007pt and LIWC 2015pt in classification tasks. Three experiments were conducted and the results indi- cate LIWC 2015pt outperforms LIWC 2007pt in all three tasks.
In many practical applications, systems and signals show energy concentration in a few coefficients. This prior knowledge can often be incorporated into algorithms designed for tasks such as compressive sensing and system identification. This Letter proposes a new least mean square (LMS)‐based algorithm that exploits the hidden sparsity of the system that the adaptive filter intends to estimate. The algorithm minimises the ℓ2‐norm of a linear transformation of the coefficient vector, using the minimum distortion principle. Simulation results demonstrate good performance of the proposed algorithm with respect to the LMS algorithm. In addition, a stochastic model of the advanced algorithm is proposed, which provides accurate mean‐square deviation and mean‐square error predictions.
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