Description Usage Arguments Details Value References Examples
This function assesses the impacts of a theme
(or subject) on the overall opinion computed for a DTD
Different themes in a DTD can be identified by the keywords
used in the DTD. These keywords (or words) can be extracted by
any analytical means available to the users, e.g.
word_imp
function. The keywords must be collated and
supplied this function through the theme_keys
argument
(see below).
1 2 3 | opi_impact(textdoc, theme_keys=NULL, metric = 1,
fun = NULL, nsim = 99, alternative="two.sided",
quiet=TRUE)
|
textdoc |
An |
theme_keys |
(a list) A one-column dataframe (of any number of length) containing a list of keywords relating to the theme or secondary subject to be investigated. The keywords can also be defined as a vector of characters. |
metric |
(an integer) Specify the metric to utilize
for the calculation of opinion score. Default: |
fun |
A user-defined function given that parameter
|
nsim |
(an integer) Number of replicas (ESD) to generate.
See detailed documentation in the |
alternative |
(a character) Default: |
quiet |
(TRUE or FALSE) To suppress processing
messages. Default: |
This function calculates the statistical
significance value (p-value
) of an opinion score
by comparing the observed score (from the opi_score
function) with the expected scores (distribution) (from the
opi_sim
function). The formula is given as
p = (S.beat+1)/(S.total+1)
, where S_total
is the total
number of replicas (nsim
) specified, S.beat
is number of replicas
in which their expected scores are than the observed score (See
further details in Adepeju and Jimoh, 2021).
Details of statistical significance of impacts
of a secondary subject B
on the opinion concerning the
primary subject A
.
(1) Adepeju, M. and Jimoh, F. (2021). An Analytical Framework for Measuring Inequality in the Public Opinions on Policing – Assessing the impacts of COVID-19 Pandemic using Twitter Data. https://doi.org/10.31235/osf.io/c32qh
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # Application in marketing:
#`data` -> 'reviews_dtd'
#`theme_keys` -> 'refreshment_theme'
#RQ2a: "Do the refreshment outlets impact customers'
#opinion of the services at the Piccadilly train station?"
##execute function
output <- opi_impact(textdoc = reviews_dtd,
theme_keys=refreshment_theme, metric = 1,
fun = NULL, nsim = 99, alternative="two.sided",
quiet=TRUE)
#To print results
print(output)
#extracting the pvalue in order to answer RQ2a
output$pvalue
|
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