Open AccessWebFeatures: A Web Tool to Extract Features from Collaborative Content
Author(s) -
Aline C. Pinto,
Beatriz S. Silva,
Priscilla R. M. Carmo,
Raphael L. A. Lima,
Larisse S. P. Amorim,
Rubio T. C. Viana,
Daniel Hasan Dalip,
Poliana A. C. Oliveira
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/webmedia_estendido.2020.13071
Subject(s) - computer science , readability , information retrieval , feature extraction , set (abstract data type) , graph , feature (linguistics) , quality (philosophy) , data mining , world wide web , artificial intelligence , linguistics , philosophy , epistemology , theoretical computer science , programming language
The production from collaborative web content has grown in recent years. Thus, exploring the quality of these data repositories has also become relevant. This work proposes to develop a tool called WebFeature. Such system allows one to manage, extract, and share quality related feature sets from text, graph and article review. To accomplish this, diff erent types of metrics were implemented based on structure, style, and readability of the texts. In order to evalu- ate the WebFeature applicability, we presented a scenario with its main functionalities (creation of a feature set, extraction of features from a known dataset, and publishing the feature set). Our demon- stration shows that this framework can be useful for extracting features automatically, supporting quality prediction of collabo- rative contents, analyzing text characterization, and improving research reproducibility.