Summary
In order to develop a proper multidimensional content analysis, we discuss some typical aspects of a pre-treatment of a textual data analysis. In particular: i) how to select the peculiar subset of the words in a text; ii) how to reduce the word ambiguity. Our proposal is to use both frequency dictionaries and reference lexicons as external lexical knowledge bases with respect to the corpus, by means of a comparison of ranking, inspired by Wegman’s parallel coordinate method. The conditions of iso-frequency of unlernmatized forms as an indication of the need for lemmatization is considered. Finally in order to evaluate the opportunities of the choices (both disambiguations and fusions), we propose the reconstruction, by means of bootstrapping strategy, of some convex hulls — as word confidence areas — in a factorial plane. Some examples from a large corpus of parliamentary discourses are presented.
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Bolasco, S. (1998). Meta-data and Strategies of Textual Data Analysis: Problems and Instruments. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_52
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DOI: https://doi.org/10.1007/978-4-431-65950-1_52
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