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Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Journal of Computer Science & Technology 2014年第3期29卷 361-375页

作者： Rafael Geraldeli Rossi Alneu de Andrade Lopes Thiago de Paulo Faleiros solange Oliveira rezende Institute of Mathematics and Computer Science University of So PauloSo CarlosBrasil

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.

关键词： heterogeneous network text classification inductive model generation

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A Process to Support Analysts in Exploring and Selecting Content from Online Forums

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Social Networking 2014年第2期3卷 86-93页

作者： Darlinton Carvalho Ricardo Marcacini Carlos Lucena solange rezende Department of Informatics Pontifical Catholic University of Rio de JaneiroRio de JaneiroBrazil Institute of Mathematical and Computer Sciences University of Sao PauloSao CarlosBrazil

The public content increasingly available on the Internet, especially in online forums, enables researchers to study society in new ways. However, qualitative analysis of online forums is very time consuming and most content is not related to researchers’ interest. Consequently, analysts face the following problem: how to efficiently explore and select the content to be analyzed? This article introduces a new process to support analysts in solving this problem. This process is based on unsupervised machine learning techniques like hierarchical clustering and term co-occurrence network. A tool that helps to apply the proposed process was created to provide consolidated and structured results. This includes measurements and a content exploration interface.

关键词： Qualitative Analysis of Online Forums Explore and Select the Online Forums Content Machine Learning Hierarchical Clustering Terms Co-Occurrence Network Consolidated and Structured Results

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