keywords relating to COVID-19 pandemics

Description

A list of keywords relating to the COVID-19 pandemic

Usage

covid_theme

Format

A dataframe containing one variable:

• keys: list of keywords

Comments on a video of a political debate.

Description

A DTD containing individual comments on a video showing the first debate between two US presidential nominees (Donald Trump and Hillary Clinton) in Sept. 2016. (Credit: NBC News).

Usage

debate_dtd

Format

A dataframe containing one variable

• text: individual text records

Details

The DTD only include the comments within the first 24hrs in which the video was posted. All individual comments in which the names of both candidates are mentioned are filtered out.

Statistical assessment of impacts of a specified theme from a DTD.

Description

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).

Usage

opi_impact(textdoc, theme_keys=NULL, metric = 1,
fun = NULL, nsim = 99, alternative="two.sided",
quiet=TRUE)

Arguments

Details

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).

Value

Details of statistical significance of impacts of a secondary subject B on the opinion concerning the primary subject A.

References

(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

Examples

# 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

Opinion score of a digital text document (DTD)

Description

Given a DTD, this function computes the overall opinion score based on the proportion of text records classified as expressing positive, negative or a neutral sentiment. The function first transforms the text document into a tidy-format dataframe, described as the ⁠observed sentiment document (OSD)⁠ (Adepeju and Jimoh, 2021), in which each text record is assigned a sentiment class based on the summation of all sentiment scores expressed by the words in the text record.

Usage

opi_score(textdoc, metric = 1, fun = NULL)

Arguments

Details

An opinion score is derived from all the sentiments (i.e. positive, negative (and neutral) expressed within a text document. We deploy a lexicon-based approach (Taboada et al. 2011) using the AFINN lexicon (Nielsen, 2011).

Value

Returns an opi_object containing details of the opinion measures from the text document.

References

(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 (2) Malshe, A. (2019) Data Analytics Applications. Online book available at: https://ashgreat.github.io/analyticsAppBook/index.html. Date accessed: 15th December 2020. (3) Taboada, M.et al. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), pp.267-307. (4) Lowe, W. et al. (2011). Scaling policy preferences from coded political texts. Legislative studies quarterly, 36(1), pp.123-155. (5) Razorfish (2009) Fluent: The Razorfish Social Influence Marketing Report. Accessed: 24th February, 2021. (6) Nielsen, F. A. (2011), “A new ANEW: Evaluation of a word list for sentiment analysis in microblogs”, Proceedings of the ESWC2011 Workshop on 'Making Sense of Microposts': Big things come in small packages (2011) 93-98.

Examples

# Use police/pandemic posts on Twitter
# Experiment with a standard metric (e.g. metric 1)
score <- opi_score(textdoc = policing_dtd, metric = 1, fun = NULL)
#print result
print(score)

#Example using a user-defined opinion score -
#a demonstration with a component of SIM opinion
#Score function (by Razorfish, 2009). The opinion
#function can be expressed as:

myfun <- function(P, N, O){
  score <- (P + O - N)/(P + O + N)
return(score)
}

#Run analysis
score <- opi_score(textdoc = policing_dtd, metric = 5, fun = myfun)
#print results
print(score)

Simulates the opinion expectation distribution of a digital text document.

Description

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).

Usage

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

Arguments

Details

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).

Value

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

References

(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

Examples

#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)

Observed sentiment document (OSD).

Description

A tidy-format list (dataframe) showing the resulting classification of each text record into positive, negative or neutral sentiment. The second column of the dataframe consists of labels variables present and absent to indicate whether any of the secondary keywords exist in a text record.

Usage

osd_data

Format

A dataframe with the following variables:

• ID: numeric id of text record with valid resultant sentiments score and classification.

• sentiment: Containing the sentiment classes.

• keywords: Indicator to show whether a secondary keyword in present or absent in a text record.

Twitter posts on police/policing

Description

A text document (an DTD) containing twitter posts (for an anonymous geographical location 'A') on police/policing. The DTD also includes posts that express sentiments on policing in relation to the COVID-19 pandemic (Secondary subject B)

Usage

policing_dtd

Format

A dataframe containing one variable

• text: individual text records

Keywords relating to facilities at train stations

Description

List of words relating to refreshments that can be found at the Piccadilly Train Station (Manchester)

Usage

refreshment_theme

Format

A dataframe containing one variable:

• keys: list of keywords

Customer reviews from tripadvisor website

Description

A text document (an DTD) containing the customer reviews of the Piccadilly train station (Manchester) downloaded from the www.tripadvisor.co.uk'. The reviews cover from July 2016 to March 2021.

Usage

reviews_dtd

Format

A dataframe containing one variable

• text: individual text records

Keywords relating to signages at train stations

Description

List of signages at the Piccadilly Train Station (Manchester)

Usage

signage_theme

Format

A dataframe containing one variable:

• keys: list of keywords

Fake Twitter posts on police/policing 2

Description

A text document (an DTD) containing twitter posts (for an anonymous geographical location 2) on police/policing (primary subject A). The DTD includes posts that express sentiments on policing in relation to the COVID-19 pandemic (Secondary subject B)

Usage

tweets

Format

A dataframe with the following variables:

• text: individual text records

• group: real/arbitrary groups of text records

Words Distribution

Description

This function examines whether the distribution of word frequencies in a text document follows the Zipf distribution (Zipf 1934). The Zipf's distribution is considered the ideal distribution of a perfect natural language text.

Usage

word_distrib(textdoc)

Arguments

Details

The Zipf's distribution is most easily observed by plotting the data on a log-log graph, with the axes being log(word rank order) and log(word frequency). For a perfect natural language text, the relationship between the word rank and the word frequency should have a negative slope with all points falling on a straight line. Any deviation from the straight line can be considered an imperfection attributable to the texts within the document.

Value

A list of word ranks and their respective frequencies, and a plot showing the relationship between the two variables.

References

Zipf G (1936). The Psychobiology of Language. London: Routledge; 1936.

Examples

#Get an \code{n} x 1 text document
tweets_dat <- data.frame(text=tweets[,1])
plt = word_distrib(textdoc = tweets_dat)

plt

Importance of words (terms) embedded in a text document

Description

Produces a wordcloud which represents the level of importance of each word (across different text groups) within a text document, according to a specified measure.

Usage

word_imp(textdoc, metric= "tf",
words_to_filter=NULL)

Arguments

Details

The function determines the most important words across various grouping of a text document. The measure options include the tf and tf-idf. The idea of tf is to rank words in the order of their number of occurrences across the text document, whereas tf-idf finds words that are not used very much, but appear across many groups in the document.

Value

Graphical representation of words importance according to a specified metric. A wordcloud is used to represent words importance if tf is specified, while facet wrapped histogram is used if tf-idf is specified. A wordcloud is represents each word with a size corresponding to its level of importance. In the facet wrapped histograms words are ranked in each group (histogram) in their order of importance.

References

Silge, J. and Robinson, D. (2016) tidytext: Text mining and analysis using tidy data principles in R. Journal of Open Source Software, 1, 37.

Examples

#words to filter out
wf <- c("police","policing")
output <- word_imp(textdoc = policing_dtd, metric= "tf",
words_to_filter= wf)