SemanticExcel.com : An online software for statistical analyses of text data based on natural language processing
(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.
(Less)
- author
- Sikström, Sverker LU ; Kjell, Oscar N.E. LU and Kjell, Katarina LU
- organization
- publishing date
- 2020
- 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
- 2024-09-19 04:27:58
@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}}, }