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A theory of term importance in automatic text analysis

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  • G. Salton
  • C. S. Yang
  • C. T. Yu

Abstract

A good deal of work has been done over the years in an attempt to use statistical or probabilistic techniques as a basis for automatic indexing and content analysis. (1–10) Unfortunately, many of these methods are lacking in effectiveness, and the more refined procedures are computationally unattractive. A new technique, known as discrimination value analysis, ranks the text words in accordance with how well they are able to discriminate the documents of a collection from each other; that is, the value of a term depends on how much the average separation between individual documents changes when the given term is assigned for content identification. The best words are those which achieve the greatest separation. The discrimination value analysis is computationally simple, and it assigns a specific role in content analysis to single words, juxtaposed words and phrases, and word groups or thesaurus categories. Experimental results are given showing the effectiveness of the technique.

Suggested Citation

  • G. Salton & C. S. Yang & C. T. Yu, 1975. "A theory of term importance in automatic text analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 26(1), pages 33-44, January.
    • Handle: RePEc:bla:jamest:v:26:y:1975:i:1:p:33-44
    DOI: 10.1002/asi.4630260106
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    Cited by:

    1. Yuetong Chen & Hao Wang & Baolong Zhang & Wei Zhang, 2022. "A method of measuring the article discriminative capacity and its distribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3317-3341, June.
    2. Janaína Gomes & Homero Dewes, 2017. "Disciplinary dimensions and social relevance in the scientific communications on biofuels," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1173-1189, March.
    3. Chung, Wingyan, 2014. "BizPro: Extracting and categorizing business intelligence factors from textual news articles," International Journal of Information Management, Elsevier, vol. 34(2), pages 272-284.
    4. Zhang, Baolong & Wang, Hao & Deng, Sanhong & Su, Xinning, 2020. "Measurement and analysis of Chinese journal discriminative capacity," Journal of Informetrics, Elsevier, vol. 14(1).
    5. Rongjiang Cai & Tao Lv & Xu Deng, 2021. "Evaluation of Environmental Information Disclosure of Listed Companies in China’s Heavy Pollution Industries: A Text Mining-Based Methodology," Sustainability, MDPI, vol. 13(10), pages 1-23, May.

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