Abstract
The lack of tools for analyzing cross-impacts of different subjects on the opinions expressed in a text document, facilitates the development of
'opitool' package. As an example, given a specific subject A and a text document downloaded with respect to it, a researcher may want to assess whether the opinion expressed concerning another subject B in relation to subject A has impacted the overall opinions on subject A in a significant way. For a real-life example, we can examine whether the public opinion expressed concerning neighbourhood policing (as subject A) has been impacted significantly by the public concerns around COVID-19 pandemic (as subject B) (see Adepeju and Jimoh, 2021). This document describes how the
opitools package has been deployed to answer the aforementioned research question.
Exploration of a text document
Following the data download, a user may wish to explore the characteristics of the word usage within the text document. “How is the pattern of word usage of a social media text document compared with a typical natural language document?” This research question can be answered by examining the log rank-frequency, i.e. the Zipf’s distribution (Zipf 1936)) plot of the document. By the Zipf’s distribution, we expect the frequency of a word contained in the document to be inversely proportional to its rank in a frequency table. The word_distrb function of opitools can be used to generate Zipf’s distribution plot (e.g Figure ).
#using a randomised Twitter data from 'opitools'
#data(tweets)
tweets_dat <- as.data.frame(tweets[,1])
plt = word_distrib(textdoc = tweets_dat)
#to show the plot, type:
#>plt$plot
For a natural language text, the Zipf’s distribution plot has a negative slope with all points falling on a straight line. Any deviation from this ideal trend line can be attributed to imperfections in the word usage. For example, the presence of a wide range of strange terms or made-up words can cause an imperfection of the text document. From Figure 1 the graph can be divided into the three sections: the upper, the middle and the lower sections. By fitting a regression line (an the ideal Zipf’s distribution), we can see what the slope of the upper section is quite different from the middle and the lower sections of the graph. The deviation at the high rank indicate an imperfection because a corpus of English language would generally contain adequate number of common words, such as ‘the’, ‘of’, and ‘at’, in order to ensure alignment on a straight line. For social media data, this deviation can suggests a significant use of a wide range of abbreviation of common words, e.g. using “&” or “nd” instead of the word “and”. Apart from the small deviation at the upper section of the graph, we can state that the law holds within most parts of our Twitter text document.
Impact Analysis
Now, in order to assess the impacts of COVID-19 pandemic (a secondary subject) on the main subject of the text document, i.e. neighbourhood policing, We need to first identify keywords that relate to the former. A user can employ any relevant analytical approach in order to identify such keywords. An example of a tool that can be used is the wordcloud, which may be used to reveal important words from within a text document.
dat <- list(tweets_dat)
series <- tibble()
#tokenize document
series <- tibble(text = as.character(unlist(dat)))%>%
unnest_tokens(word, text)%>% #tokenize
dplyr::select(everything())
#removing stopwords
tokenize_series <- series[!series$word %in% stopwords("english"),]
#compute term frequencies
doc_words <- tokenize_series %>%
dplyr::count(word, sort = TRUE) %>%
dplyr::ungroup() %>%
dplyr::mutate(len=nchar(word)) %>%
#remove words with character length <= 2
dplyr::filter(len > 2)%>%
data.frame() %>%
dplyr::rename(freq=n)%>%
dplyr::select(-c(len))%>%
#removing the words, '' & '' because of
#their dominance
dplyr::filter(!word %in% c("police", "policing"))
row.names(doc_words) <- doc_words$word
#use only the top 1000 words
wordcloud2(data=doc_words[1:1000,], size = 0.7, shape = 'pentagon')
From the wordcloud (i.e. 2), the size of the words represent their respective frequencies (importance) across the document. Keywords relating to the COVID-19 pandemic are circled in red. In similar fashion, a user can identify keywords that relate to several other subjects that may have impacted neighbourhood policing during the data period. A list of COVID-19 pandemic related keywords are supplied with the opitools package. They can be assessed by typing:
> covid_keys
# keys
#1 pandemic
#2 pandemics
#3 lockdown
#4 lockdowns
#5 corona
#6 coronavirus
#7 covid
#8 covid19
#9 covid-19
#10 virus
#11 viruses
#12 quarantine
#13 infect
#14 infects
#15 infecting
#16 infected
The impact analysis can be performed as follows:
# call data
data(tweets)
# Get an n x 1 text document
tweets_dat <- as.data.frame(tweets[,1])
# Run the analysis
output <- opi_impact(tweets_dat, sec_keywords=covid_keys, metric = 1,
fun = NULL, nsim = 99, alternative="two.sided",
quiet=TRUE)
To print results:
output
#> $test
#> [1] "Test of significance (Randomization testing)"
#>
#> $criterion
#> [1] "two.sided"
#>
#> $exp_summary
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -27.80 -26.52 -26.10 -26.13 -25.75 -24.26
#>
#> $p_table
#>
#>
#> observed_score S_beat nsim pvalue signif
#> --------------- ------- ----- ------- -------
#> -28.23 0 99 0.01 ***
#>
#> $p_key
#> [1] "0.99'" "0.05*" "0.025**" "0.01***"
#>
#> $p_formula
#> [1] "(S_beat + 1)/(nsim + 1)"
The output shows that COVID-19 pandemic has had a significant impacts on the public opinion concerning neighbourhood policing. This is indicated by the opinion scores -28.23 and a pvalue of 0.01. To display the graphics showing the proportion of various sentiment classes (as in Figure ), type output$plot in the console.
Using a user-defined opinion score function
As the definition of opinion score function may vary from one application field to another, a user can specify a pre-defined opinion score function. For instance, (Razorfish 2009) defines opinion score of a product brand as score = (P + O - N)/(P + O + N), where P, O, and N, represent the amount/proportion of positive, neutral and negative, sentiments, respectively. Using a user-define function, the analysis can be re-run as follows:
First define the function:
#define opinion score function
myfun <- function(P, N, O){
score <- (P + O - N)/(P + O + N)
return(score)
}
Re-run impact analysis
results <- opi_impact(tweets_dat, sec_keywords=covid_keys, metric = 5,
fun = myfun, nsim = 99, alternative="two.sided",
quiet=TRUE)
Print results:
print(results)
#> $test
#> [1] "Test of significance (Randomization testing)"
#>
#> $criterion
#> [1] "two.sided"
#>
#> $exp_summary
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -27.80 -26.52 -26.10 -26.13 -25.75 -24.26
#>
#> $p_table
#>
#>
#> observed_score S_beat nsim pvalue signif
#> ------------------- ------- ----- ------- -------
#> -0.234129692832764 99 99 1 NA
#>
#> $p_key
#> [1] "0.99'" "0.05*" "0.025**" "0.01***"
#>
#> $p_formula
#> [1] "(S_beat + 1)/(nsim + 1)"
Based on the user defined opinion score function, the new opinion score is estimated as -0.234, while the pvalue now equals to 1 (non-significant). This implies that the outcome of whether a secondary subject has had a significant impact on the primary subject is also dependent on the opinion score function specified.
Conclusion
The opitools package has been developed in order to aid the replication of the study (Adepeju and Jimoh 2021) for other application fields. In essence, the utility of the functions contained in this package is not limited to law enforcement(s) and public health, but rather can be applicable to several other public services more generally. This package is being updated on a regular basis to add more functionalities.
We encourage users to report any bugs encountered while using the package so that they can be fixed immediately. Welcome contributions to this package which will be acknowledged accordingly.
