opi_sim: Simulates the opinion expectation distribution of a digital...
In opitools: Analyzing the Opinions in a Big Text Document

Description Usage Arguments Details Value References Examples

This function simulates the expectation distribution of the observed opinion score (computed using the opi_score function). The resulting tidy-format dataframe can be described as the expected sentiment document (ESD) (Adepeju and Jimoh, 2021).

1	opi_sim(osd_data, nsim=99, metric = 1, fun = NULL, quiet=TRUE)

`osd_data`	A list (dataframe). An `n` x `3` OSD, in which `n` represents the length of the text records that have been successfully classified as expressing positive, negative or a neutral sentiment. Column `1` of the OSD is the text record ID, column `2` shows the sentiment classes (i.e. positive, negative, or neutral), while column `3` contains two variables: `present` and `absent` indicating records that include and records that do not include any of the specified theme keywords, respectively.
`nsim`	(an integer) Number of replicas (ESD) to simulate. Recommended values are: 99, 999, 9999, and so on. Since the run time is proportional to the number of replicas, a moderate number of simulation, such as 999, is recommended. Default: `99`.
`metric`	(an integer) Specify the metric to utilize for the calculation of the opinion score. Default: `1`. See details in the documentation of `opi_score` function. The input argument here must correspond to that of `opi_score` function in order to compute a statistical significance value (p-value).
`fun`	A user-defined function given that parameter `metric` is set equal to `5`. See details in the documentation of the `opi_score` function.
`quiet`	(TRUE or FALSE) To suppress processing messages. Default: `TRUE`.

Employs non-parametric randomization testing approach in order to generate the expectation distribution of the observed opinion scores (see details in Adepeju and Jimoh 2021).

Returns a list of expected opinion scores with length equal to the number of simulation (nsim) specified.

(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

#Prepare an osd data from the output
#of `opi_score` function.

score <- opi_score(textdoc = policing_dtd,
                     metric = 1, fun = NULL)
#extract OSD
OSD <- score$OSD
#note that `OSD` is shorter in length
#than `policing_dtd`, meaning that some
#text records were not classified

#Bind a fictitious indicator column
osd_data2 <- data.frame(cbind(OSD,
           keywords = sample(c("present","absent"), nrow(OSD),
           replace=TRUE, c(0.35, 0.65))))

#generate expected distribution
exp_score <- opi_sim(osd_data2, nsim=99, metric = 1,
                                 fun = NULL, quiet=TRUE)
#preview the distribution
hist(exp_score)