Abstract
In this chapter, we introduce latent semantic analysis (LSA), which uses singular value decomposition (SVD) to reduce the dimensionality of the document-term representation. This method reduces the large matrix to an approximation that is made up of fewer latent dimensions that can be interpreted by the analyst. Two important concepts in LSA, cosine similarity and queries, are explained. Finally, we discuss decision-making in LSA.
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Notes
- 1.
Note: The qT vector is created using binary frequency, because at this stage weighting cannot be calculated and applied to the pseudo-document.
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Further Reading
For more about latent semantic analysis (LSA), see Landauer et al. (2007).
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Anandarajan, M., Hill, C., Nolan, T. (2019). Semantic Space Representation and Latent Semantic Analysis. In: Practical Text Analytics. Advances in Analytics and Data Science, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-95663-3_6
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DOI: https://doi.org/10.1007/978-3-319-95663-3_6
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