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An Analysis of Constructed Categories for Textual Classification Using Fuzzy Similarity and Agglomerative Hierarchical Methods

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Emergent Web Intelligence: Advanced Semantic Technologies

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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Abstract

Ambiguity is a challenge faced by systems that handle natural language. To assuage the issue of linguistic ambiguities found in text classification, this work proposes a text categorizer using the methodology of Fuzzy Similarity. The clustering algorithms Stars and Cliques are adopted in the Agglomerative Hierarchical method and they identify the groups of texts by specifying some type of relationship rule to create categories based on the similarity analysis of the textual terms. The proposal is based on the methodology suggested, categories can be created from the analysis of the degree of similarity of the texts to be classified, without needing to determine the number of initial categories. The combination of techniques proposed in the categorizer’s steps brought satisfactory results, proving to be efficient in textual classification.

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Notes

  1. 1.

    Stopwords are closed classes of words that do not carry meaning, such as articles, pronouns, interjections and prepositions.

  2. 2.

    Corpus extracted from Terra Networks Brasil S/A.

  3. 3.

    The complete collection has 1,578 texts, however, these files were not available for use in their totality. Hence, we used only the 100 texts that are available online.

  4. 4.

    These files, which come from the most diverse RSS channels of Terra Networks Brasil S/A, were collected daily during the period comprising February to March 2008.

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Author information

Authors and Affiliations

  1. Departamento de Ciência da Computação, Instituto de Computação—IC, Universidade Federal Fluminense—UFF, Rua Passo da Pátria 156, Bloco E, 3º andar, São Domingos, Niterói, RJ CEP 24210-240, Brazil

    Marcus V. C. Guelpeli & Ana Cristina Bicharra Garcia

  2. Departamento de Ciência e Tecnologia—RCT, Pólo Universitário de Rio das Ostras—PURO, Universidade Federal Fluminense—UFF, Rua Recife, s/n, Jardim Bela Vista, Rio das Ostras, RJ CEP 28890-000, Brazil

    Flavia Cristina Bernardini

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  1. Marcus V. C. Guelpeli
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  2. Ana Cristina Bicharra Garcia
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  3. Flavia Cristina Bernardini
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Correspondence to Marcus V. C. Guelpeli .

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Editors and Affiliations

  1. INSA de Lyon, avenue Jean Capelle 7, Villeurbanne CX, 69621, France

    Youakim Badr

  2. Fac. Sciences Mirande, UMR CNRS 5158, Université de Bourgogne, Dijon CX, France

    Richard Chbeir

  3. Technology, Center for Quantifiable Quality of, Norwegian University of Science &, O.S. Bragstads plass 2E, Trondheim, 7491, Norway

    Ajith Abraham

  4. College of Business & Administration, Dept. Quantitative Methods &, Kuwait University, Safat, Kuwait

    Aboul-Ella Hassanien

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Guelpeli, M.V.C., Bicharra Garcia, A.C., Bernardini, F.C. (2010). An Analysis of Constructed Categories for Textual Classification Using Fuzzy Similarity and Agglomerative Hierarchical Methods. In: Badr, Y., Chbeir, R., Abraham, A., Hassanien, AE. (eds) Emergent Web Intelligence: Advanced Semantic Technologies. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-077-9_11

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  • DOI: https://doi.org/10.1007/978-1-84996-077-9_11

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-076-2

  • Online ISBN: 978-1-84996-077-9

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