The indexed Web increases every day, making the development of automatic methods for knowledge extraction more relevant. The area of Sentiment Analysis or Opinion Mining aims to extract opinions from the user-generated content and to define the semantic orientation of each individual opinion. This work proposes an approach to estimate the degree of importance of comments generated by web users by using a Fuzzy system. The Fuzzy system has three inputs: author reputation, number of tuples f eature, quality word , and percentage of correctly spelled words and one output: importance degree of the comment. The importance degree has been used to select the best comments in a Corpus. The paper also describes an experiment which was used to compare the results of a sentiment orientation method before and after the selection of the best comments. It was conducted with 1620 reviews also about smartphones (982 positives and 594 negatives) and our approach improved the results of sentiment orientation method up to approximately 10% in f-measure in positive reviews and 20% in f-measure in negative reviews.

Recently, several reports suggest differences in the vascularization of the various histopathologic patterns of parenchymal remodeling seen in usual interstitial pneumonia (UIP). In this study, we sought to validate the importance of vascular remodeling in patients with idiopathic pulmonary fibrosis (IPF) and to examine the relationship between vascular remodeling and parenchymal remodeling or pulmonary function. Open lung biopsies were performed in 57 patients with IPF, and vascular changes in alternating areas of parenchymal remodeling (UIP histologic patterns) were studied. Quantitative analysis of the internal area, internal perimeter, wall thickness, and surrounding cellularity of medium or large pulmonary arteries, as well as their distribution according to air/parenchymal ratios, was performed. Semiquantitative analysis also was used to determine the grade of vascular occlusion. An inverse association was found between vascularization and UIP parenchymal remodeling (p < 0.05); that is, the decreased internal luminal area and perimeter as well as the increased wall thickness run in parallel with progression from alveolar collapse toward severe mural-organizing fibrosis with honeycombing. Vascular regression (diminished internal area and perimeter of vessels) was also associated with higher FEV(1), FVC, and RV values (r = 0.48, p< 0.05), reflecting a tight relationship between vascular remodeling and pulmonary function. A progressive regression of vascularization, reflected by different degrees of luminal occlusion after vascular remodeling, coincided with parenchymal remodeling (alveolar collapse, mural-organizing fibrosis, and honeycombing). This vascular regression may be responsible for the impaired wound healing and progressive fibroproliferation found in patients with IPF. Further studies are needed to determine whether this relationship is causal or consequential.

No abstract

Paraphrase detection is a Natural-Language Processing (NLP) task that aims at automatically identifying whether two sentences convey the same meaning (even with different words). For the Portuguese language, most of the works model this task as a machine-learning solution, extracting features and training a classifier. In this paper, following a different line, we explore a graph structure representation and model the paraphrase identification task over a heterogeneous network. We also adopt a back-translation strategy for data augmentation to balance the dataset we use. Our approach, although simple, outperforms the best results reported for the paraphrase detection task in Portuguese, showing that graph structures may capture better the semantic relatedness among sentences.

Predicting review helpfulness is an important task in Natural Language Processing. It is useful for dealing with the huge amount of online reviews on varied domains and languages, helping and guiding users on what to read and consider in their daily decisions. However, there are limited initiatives to investigate the nature of this task and how hard it is. This paper aims to fulfill this gap, providing a better understanding of it. Two complementary experiments are performed in order to uncover patterns of usefulness evaluation as performed by humans and relevant features for machine prediction. To assure our results, we run the experiments for two different domains: movies and apps. We show that humans agree on the process of assigning helpfulness to reviews, despite the difficulty of the task. More than this, people perform this process systematically and consistently. Finally, we empirically identify the most relevant content features for machine learning prediction of review helpfulness.

The evolution of e-commerce has contributed to the increase of the information available, making the task of analyzing the reviews manually almost impossible. Due to the amount of information, the creation of automatic methods of knowledge extraction and data mining has become necessary. Currently, to facilitate the analysis of reviews, some websites use filters such as votes by the utility or by stars. However, the use of these filters is not a good practice because they may exclude reviews that have recently been submitted to the voting process. One possible solution is to filter the reviews based on their textual descriptions, author information, and other measures. This chapter has a propose of approaches to estimate the importance of reviews about products and services using fuzzy systems and artificial neural networks. The results were encouraging, obtaining better results when detecting the most important reviews, achieving approximately 82% when f-measure is analyzed.

No abstract

No abstract