An R-package for analyzing Opinions in Big Digital Text Document (DTD)
The opitools is an R package for exploring a digital
text document (DTD) as well as performing the impact analysis of the
opinions expressed by the DTD. The package is particularly suited for
opinion-based DTD, such as individual-level posts (e.g. commentaries on
Twitter or Facebook) and comments (as in an online product reviews).
First, an exploratory function word_imp can be
used to identify words relating to different themes (or subjects) that
are referenced in a DTD. The opi_impact function can then
be utilized to investigate whether an identified/specified theme (or
subject) impacts the overall opinion expressed by the document. The
potentials of opitools for application across a wide range
of domains is described briefly here (see the vignette for
details).
CRANFrom an R console, type:
#install.packages("opitools")
library(opitools)
To install the developmental version of the package, type:
remotes::install_github("MAnalytics/opitools"). Please,
report any installation problems in the issues.
Below is an example usage of how opitools can be
employed to identify themes (or subjects) from a DTD and then deployed
to perform opinion impact analysis.
The policing_dtd - a DTD comprising Twitter posts about
police/policing in a neighbourhood, will be used in this
demonstration.
> data(policing_dtd)Utilize word_imp function to highlights terms or words
in accordance to their importance in the DTD. Through visual inspection,
collate related terms or words that denote specific theme or subject
from the generated wordcloud. Several words relating to the COVId-19
pandemic, including ‘infect’, ‘pandemic’, and ‘lockdown’ can be
identified as been important in the document. These words are provided
as covid_theme in the package. The function can be ran as
follows (see the documentation for details):
> p1a <- word_imp(textdoc = policing_dtd, metric= "tf",
words_to_filter=c("police","policing"))The impact analysis can be conducted as follows:
#Running the analysis
results <- opi_impact(textdoc = policing_dtd, theme_keys=covid_theme, metric = 1,
fun = NULL, nsim = 99, alternative="two.sided",
pplot = TRUE, quiet=FALSE)
print(results)
$test
[1] "Test of significance (Randomization testing)"
$criterion
[1] "two.sided"
$exp_summary
Min. 1st Qu. Median Mean 3rd Qu. Max.
-8.240 -5.880 -5.880 -5.028 -3.530 -1.180
$p_table
|observed_score |S_beat |nsim |pvalue |signif |
|:--------------|:------|:----|:-------|:------|
|-5.88 |56 |99 |0.52 |' |
$p_key
[1] "0.99'" "0.05*" "0.025**" "0.01***"
$p_formula
[1] "(S_beat + 1)/(nsim + 1)"
#......The research question of the analysis above can be stated as follows:
RQ1: “Does COVID-19 pandemic influence public opinion on neighourhood policing?”
The output shows an overall negative opinion (-5.88) of
the public on the neighbourhood policing, and that the pandemic has not
had a significant impacts (pvalue = 0.52) on the opinion
expressed by the public. (More detailed explanation can be found in the
study adepeju, M. and
Jimoh, F. (2021)).
Table 1 summarizes the analysis using different example datasets provided in the package. The output from the law enforcement application (as in above) is entered in the first row of the table. Other research questions investigated are as follow:
RQ2a: “Do the refreshment outlets/items impact customers’ opinion of the Piccadilly train services?”
RQ2b: “Do the signages influence customers’ opinion of the Piccadilly train services?”
RQ3: “How does the democratic candidate (Hillary Clinton) affects viewers’ opinion of the presidential debate?”
Table 1. Impact analysis results
| RQs | Primary data | Theme_keys | Score function | Observed Score (S) | P-value |
|:-----: | :----------: | :---------------: | :-------------:| :-----------------:| :---------:|
| RQ1 | policing_dtd | covid_theme | '1' | -5.88 | 0.52 |
| RQ2a | reviews_dtd | refreshment_theme | '1' | 67.92 | 0.01 |
| RQ2b | reviews_dtd | signage_theme | '1' | 67.92 | 0.1 |
| RQ3 | debate_dtd | direct input | '1' | -0.33 | 0.93 |In each example, the same opinion score function is employed
(metric = 1, i.e. the
polarity score = (P - N)/(P + N)*100, where P
and N represent positive and
negative sentiments, respectively). See the documentation
for details. Employing a threshold of p=0.05, any p-values
less or equal to the threshold (e.g. RQ2a) represent a significant
impact of the specified theme (i.e. refreshment_theme) on
the overall opinion score computed based on the
reviews_dtd.