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SemanticExcel.com : An online software for statistical analyses of text data based on natural language processing

Sikström, Sverker LU orcid ; Kjell, Oscar N.E. LU and Kjell, Katarina LU (2020) p.87-103
Abstract

The overall aim of this chapter is to present a guide in how to efficiently measure and statistically analyze text and numerical data using the online software SemanticExcel.com; we will focus on the following main functions: 1. Computing semantic similarity scores between the semantic representations of two sets of texts. 2. Testing whether the semantic representations of two sets of texts statistically differ using a paired semantic t-test. 3. Train the semantic representations to predict numerical values. 4. Predicting numerical values from the semantic representations of texts by applying a semantic trained (valence) model. 5. Visualize words based on statistically significant relationships along y and x-axes. Exercises are provided... (More)

The overall aim of this chapter is to present a guide in how to efficiently measure and statistically analyze text and numerical data using the online software SemanticExcel.com; we will focus on the following main functions: 1. Computing semantic similarity scores between the semantic representations of two sets of texts. 2. Testing whether the semantic representations of two sets of texts statistically differ using a paired semantic t-test. 3. Train the semantic representations to predict numerical values. 4. Predicting numerical values from the semantic representations of texts by applying a semantic trained (valence) model. 5. Visualize words based on statistically significant relationships along y and x-axes. Exercises are provided for you to practice on in the end.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Statistical Semantics : Methods and Applications - Methods and Applications
pages
17 pages
publisher
Springer International Publishing
external identifiers
  • scopus:85089327818
ISBN
9783030372507
9783030372491
DOI
10.1007/978-3-030-37250-7_6
language
English
LU publication?
yes
id
8c835304-ec7c-4abc-8978-703b1f129ae6
date added to LUP
2020-08-24 08:33:50
date last changed
2024-06-12 19:13:23
@inbook{8c835304-ec7c-4abc-8978-703b1f129ae6,
  abstract     = {{<p>The overall aim of this chapter is to present a guide in how to efficiently measure and statistically analyze text and numerical data using the online software SemanticExcel.com; we will focus on the following main functions: 1. Computing semantic similarity scores between the semantic representations of two sets of texts. 2. Testing whether the semantic representations of two sets of texts statistically differ using a paired semantic t-test. 3. Train the semantic representations to predict numerical values. 4. Predicting numerical values from the semantic representations of texts by applying a semantic trained (valence) model. 5. Visualize words based on statistically significant relationships along y and x-axes. Exercises are provided for you to practice on in the end.</p>}},
  author       = {{Sikström, Sverker and Kjell, Oscar N.E. and Kjell, Katarina}},
  booktitle    = {{Statistical Semantics : Methods and Applications}},
  isbn         = {{9783030372507}},
  language     = {{eng}},
  pages        = {{87--103}},
  publisher    = {{Springer International Publishing}},
  title        = {{SemanticExcel.com : An online software for statistical analyses of text data based on natural language processing}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-37250-7_6}},
  doi          = {{10.1007/978-3-030-37250-7_6}},
  year         = {{2020}},
}