UPDATE: 2020-04-14: I added a new function (textConversationAnalysis) that gives some very basic and descriptive conversation metrics from either the video transcript or the chat file.
You can always access the most recent version of the functions by including the statement source(“http://apknight.org/zoomGroupStats.R”) at the top of your code.
Alternatively, you could go to http://apknight.org/zoomGroupStats.R and copy/paste the code into your editor.
and copy/paste the code into your editor.
ORIGINALÂ POSTÂ FOLLOWS:
In response to the shift to so many online meetings, I created a set of R functions to help do research using web-based meetings. In brief, these functions use the output of recorded sessions (e.g., video feed, transcript file, chat file) to do things like sentiment analysis, face analysis, and emotional expression analysis. In the coming week, I will extend these to do basic conversation analysis (e.g., who speaks/chats most, turntaking).
I went overboard in commenting the code so that hopefully others can use them. But, if you’re still having trouble getting them to work, please don’t hesitate to reach out to me.
You can directly access the functions here:
http://apknight.org/zoomGroupStats.R
After reviewing this, you could call these functions using the following statement in R: source(“http://apknight.org/zoomGroupStats.R”)
############################################################
# Author: Andrew Knight (http://apknight.org)
# Last Update: 2020-04-14 13:00 US CDT
# Update Note: Added the textConversationAnalysis function
# I created this as a way to help people do social science research through web-based meetings (i.e., Zoom).
# It's still a work in progress, but this is a start. If you would like to use it or help build it,
# please reach out!
############################################################
# OVERVIEW OF FUNCTIONS
############################################################
# This script contains functions to use for analyzing recorded Zoom sessions
# This is a work in progress and more are coming.
# processZoomChat Parses the downloaded chat file from a recorded Zoom session
# processZoomTranscript Parses the downloaded transcript from a recorded Zoom session
# sentiOut Conducts a sentiment analysis on either the Chat or Transcript
# videoFaceAnalysis Analyzes the video from a Zoom session and outputs face/emotion measures
# textConversationAnalysis Analyzes either chat or transcript and outputs conversation metrics
# Note you will require the following packages to run these:
# reshape2
# stringr
# paws
# magick
# data.table
# You will also require an aws account with privileges for rekognition and comprehend to use the
# text analysis and video analysis. If you don't know how to do this, please:
# Search online for (a) setting up AWS account; (b) setting up paws. I found the following useful:
# https://github.com/paws-r/paws/blob/master/docs/credentials.md
# If, after you try you are still struggling, I can give guidance on this if useful--just contact me.
############################################################
# processZoomChat Function
############################################################
# Zoom Chat File Processing
# This function parses the data from the chatfile that is downloaded from the Zoom website.
# NOTE: This is the file that accompanies a recording. This is not the file
# that you download directly within the window. It is also not the one that is
# saved locally on your computer. This is the file that you can access after a session
# if you record in the cloud.
# example call: ch.out = processZoomChat(fname="~/Desktop/chat.txt", sessionStartDateTime="2020-04-01 17:56:34", languageCode="en")
# INPUT ARGUMENTS:
# fname: the path to the local file where the chat file (txt) is saved.
# sessionStartDateTime: the time that the actual session was launched. Format is YYYY-MM-DD HH:MM:SS
# languageCode: the code for the language (e.g., en)
# OUTPUT:
# message_id: an incremented numeric identifier for a message in the chat
# message_time: a timestamp for the message, based on the start of the Zoom session
# user_name: the name attached to the message
# message: the text of the message
# message_language: the language code for the chat
processZoomChat = function(fname, sessionStartDateTime, languageCode) {
require(reshape2)
require(stringr)
ch = read.delim(fname, sep="\t", stringsAsFactors=F, header=F, col.names=c("message_increment", "user_name", "message"))
####################################
# First thing do to is to create a message_time variable
# This is user-supplied and could come from the usermeeting report that can be downloaded
sessionStartDateTime = as.POSIXct(sessionStartDateTime)
# This is the value embedded in the chat record. It is an HH:MM:SS delta from the start of the session
# I'm doing a crude parse to just get the total number of seconds that the delta is. This is then
# used as the increment from the sessionStartDateTime
ch$inc_hours = as.numeric(substr(ch$message_increment,1,2))
ch$inc_mins = as.numeric(substr(ch$message_increment,4,5))
ch$inc_secs = as.numeric(substr(ch$message_increment,7,8))
ch$inc_total_secs = ch$inc_hours*60*60 + ch$inc_mins*60 + ch$inc_secs
ch$message_time = sessionStartDateTime + ch$inc_total_secs
####################################
# Chat transcripts do not handle soft returns well (i.e., if the same person uses a soft line break
# for multiple lines in a single message that is submitted to the system).
# This is a crude way to identify them based on someone having an invalid message time.
# For now, will assign that text to the last marked user name in the dataset,
# pasting the messages together into a single line (separated by a space. )
# Create a flag to mark erroneous records based on the message time variable. This should be made stronger
# and cleaner eventually
ch$flag = is.na(as.integer(substr(ch$message_increment,1,1))) + (nchar(ch$message_increment) != 8)
# Assign the value in the message_increment variable to the message variable. This is because
# the parsing of the file is fucked up when there are soft returns in someone's chat message
ch$message = ifelse(ch$flag > 0, ch$message_increment, ch$message)
# Go through the records from the bottom up to paste the message on the one it
# should be part of
for(i in nrow(ch):1) {
if(ch[i,"flag"] > 0) {
ch[(i-1), "message"] = paste(ch[(i-1), "message"], ch[i, "message"], sep=" ")
}
}
# now drop the unnecessary records
ch = ch[ch$flag == 0, ]
# get rid of whitespace at the beginning and end
ch$message = str_trim(ch$message, "both")
# Add a language variable, which is user-supplied for now
ch$message_language = languageCode
# Add a simple numeric incrementing identifier for the messages that people submitted
ch$message_id = 1:nrow(ch)
# Get rid of the superfluous colon at the end of the usernames
ch$user_name = substr(ch$user_name, 1, nchar(ch$user_name)-1)
# Clean up the ordering of variables that are returned
ch = ch[,c("message_id", "message_time", "user_name", "message", "message_language")]
return(ch)
}
############################################################
# processZoomTranscript Function
############################################################
# Zoom Recording Transcript File Processing
# This function parses the data from the transcript file (.vtt) that is downloaded from the Zoom website.
# NOTE: This is the file that accompanies a recording to the cloud.
# example call: tr.out = processZoomTranscript(fname="~/Desktop/transcript.vtt", recordingStartTime="2020-04-01 17:56:34", languageCode="en")
# INPUT:
# fname: the path to the local file where the transcript file (vtt) is saved.
# recordingStartDateTime: the time that the recording was launched. Format is YYYY-MM-DD HH:MM:SS
# languageCode: the code for the language (e.g., en)
# Note: I plan to fix at a later point in time the timing issue. Specifically, it is not clear
# where in Zoom's system I can get the actual time that the recording was started. This
# is a problem for linking the transcript file up with the chat file.
# One workaround for now (for research) would be to set recordings to auto-start. This is not ideal, though.
# we should be able to know when the recording was started. It is embedded in the video, so could pull from there.
# OUTPUT:
# utterance_id: an incremented numeric identifier for a marked speech utterance
# utterance_start_time: the timestamp for the start of the utterance
# utterance_end_time: the timestamp for the end of the utterance
# utterance_time_window: the number of seconds that the utterance took
# user_name: the name attached to the utterance
# utterance_message: the text of the utterance
# utterance_language: the language code for the transcript
processZoomTranscript = function(fname, recordingStartDateTime, languageCode) {
library(reshape2)
require(stringr)
# Parse the transcript file -- vtt is a structured format.
f = readLines(fname)
# there are three main pieces of data for each marked "utterance" - an id, a window of time, and the text
utterance_id = as.integer(f[seq(3,length(f), 4)])
utterance_window = f[seq(4,length(f), 4)]
utterance_text = f[seq(5,length(f), 4)]
# Parse the time window into two separate elements
utterance_start_time = unlist(strsplit(utterance_window, " --> "))[seq(1, length(utterance_window)*2, 2)]
utterance_end_time = unlist(strsplit(utterance_window, " --> "))[seq(2, length(utterance_window)*2, 2)]
# Just a little function to do this X 2
timeCalc = function(startTime, incTime) {
inc_hours = as.numeric(substr(incTime,1,2))
inc_mins = as.numeric(substr(incTime,4,5))
inc_secs = as.numeric(substr(incTime,7,12))
inc_total_secs = inc_hours*60*60 + inc_mins*60 + inc_secs
correct_time = startTime + inc_total_secs
}
# Now turn these into actual datetime values
recordingStartDateTime = as.POSIXct(recordingStartDateTime)
utterance_start_time = timeCalc(recordingStartDateTime, utterance_start_time)
utterance_end_time = timeCalc(recordingStartDateTime, utterance_end_time)
# Create a time window (in seconds) for the utterances -- how long is each in seconds
utterance_time_window = as.numeric(difftime(utterance_end_time, utterance_start_time, units="secs"))
# Parse the utterance message itself
utterance_message = substring(utterance_text, regexpr("[:]", utterance_text)+2)
# Get the user name that spoke the text
user_name = substr(utterance_text, 1, regexpr("[:]", utterance_text)-1)
# Prepare the output file
res.out = data.frame(utterance_id, utterance_start_time, utterance_end_time, utterance_time_window, user_name, utterance_message, stringsAsFactors=F)
# Add the language code
res.out$utterance_language = languageCode
return(res.out)
}
############################################################
# sentiOut Function
############################################################
# Text-based sentiment analysis function
# This function takes in the output of the chat and transcript functions. It then
# conducts a sentiment analysis on an identified chunk of text
# and returns the values.
# To use this function, you must have an aws account that with privileges for the comprehend service
# However you authenticate for AWS, you should do so before running the function.
# example call: sent.out = sentiOut(inputData=tr.out, idVar = "utterance_id", textVar = "utterance_message", languageCodeVar = "utterance_language")
# INPUT:
# inputData: the input data frame
# idVar: the name of the id variable for the text
# textVar: the name of the variable with the text in it
# languageCodeVar: the variable containing the language code for each text chunk
# OUTPUT: This function returns the inputData plus the following variables
# sent_class: the text-based sentiment classification of the message
# Mixed: the confidence level for the text being mixed sentiment
# Negative: the confidence level for the text being negative sentiment
# Neutral: the confidence level for the text being neutral sentiment
# Positive: the confidence level for the text being positive sentiment
# Note: This function currently does this in a brute force way. In the future, I will
# build this so that it batches chunks of text to run, rather than looping through.
sentiOut = function(inputData, idVar, textVar, languageCodeVar){
require(paws)
require(reshape2)
# Identify the AWS service comprehend:
# AS STATED ABOVE--YOU MUST HAVE AN AUTHENTICATED ACCOUNT WITH THE RIGHT PRIVILIGES
svc = comprehend()
# Loop through each record of the inputData
for(i in 1:nrow(inputData)) {
# Run the sentiment detection function from AWS Comprehend on this chunk of text
sent = svc$detect_sentiment(Text = inputData[i,textVar], LanguageCode=inputData[i,languageCodeVar])
# Create a simple
res.line = cbind(inputData[i,idVar],unlist(sent$SentimentScore), sent$Sentiment)
if(i == 1) {
res.out = res.line
} else {
res.out = rbind(res.out, res.line)
}
}
# Now, clean up the output so that it comes as a dataframe
d.res = data.frame(res.out, stringsAsFactors=F)
names(d.res) = c(idVar, "sent_value", "sent_class")
d.res$sent_type = unlist(lapply(strsplit(row.names(d.res), '[.]'), '[[',1))
d.res.melt = reshape2::melt(d.res, idVars=c(idVar, sent_class, "sent_type"), variable.name="sent_variable", value.name="sent_value")
d.res.wide = reshape2::dcast(d.res.melt, get(idVar) + sent_class ~ sent_type, value.var="sent_value")
names(d.res.wide)[1] = idVar
d.res.wide[,c("Mixed", "Negative", "Neutral", "Positive")] = lapply(d.res.wide[,c("Mixed", "Negative", "Neutral", "Positive")], as.numeric)
d.mrg = merge(inputData, d.res.wide, by=idVar, all.x=T)
return(d.mrg)
}
############################################################
# videoFaceAnalysis Function
############################################################
# Video-based sentiment analysis function
# This function takes in a video file and produces measures based on the faces in the
# video. Note that this is a very crude way of doing this. It uses the image detection
# capabilities in AWS rekognition to cut the video up in to a sample of frames, then
# analyzes those frames. rekognition has a video analysis feature that I'll incorporate
# later.
# For best results with recorded Zoom sessions:
# I would recommend going into your settings for recordings and choosing to
# record active speaker, gallery view, and shared screen separately.
# Make sure to choose to record the gallery view so that you get
# all of the faces in a single video feed. You also might want to choose "Optimize the recording..."
# To use this function, you must have an aws account that with privileges for the rekognition service
# However you authenticate for AWS, you should do so before running the function.
# example call: vid.out = videoFacesAnalysis(inputData="~/Desktop/sample_video.mp4", recordingStartTime="2020-04-01 17:56:34", sampleWindow=20, facesCollectionID="class15-r")
# INPUT ARGUMENTS:
# inputVideo: the input video file
# recordingStartDateTime: the name of the id variable for the text
# sampleWindow: the number of seconds in between each sample of the recording
# facesCollectionID: Not necessary: Name of an S3 collection if you want to ID specific people
# OUTPUT:
# frame_id an identifier for the frame of the video used for this record
# img_timestamp the timestamp of the image from the video (see note below re: recording)
# identified_person the name of the person identified in the frame, if a collection is given
# identification_confidence the confidence level for the identity (first one)
# face_id an identifier for the face in the frame
# age_low low boundary for estimated age
# age_high high boundary for estimated age
# smile boolean - does the face have smile
# eyeglasses boolean - does the face have eyeglasses
# sunglasses boolean - does the face have sunglasses
# gender gender of face
# beard boolean - does the face have beard
# mustache boolean - does the face have mustache
# eyesopen boolean - does the face have eyes open
# mouthopen boolean - does the face have mouth open
# confused confidence level for the face showing confused
# calm confidence level for the face showing calm
# happy confidence level for the face showing happy
# disgusted confidence level for the face showing disgusted
# angry confidence level for the face showing angry
# fear confidence level for the face showing fear
# sad confidence level for the face showing sad
# surprised confidence level for the face showing surprised
# Note: This function currently es things in a brute force way. I'll refine this so that
# things are processed in batch, rather than in so many gross loops.
# Note: Same as with transcripts: I plan to fix at a later point in time the timing issue. Specifically, it is not clear where in Zoom's system I can get the actual time that the recording was started. This
# is a problem for linking the transcript file up with the chat file.
# One workaround for now (for research) would be to set recordings to auto-start. This is not ideal, though.
# we should be able to know when the recording was started. It is embedded in the video, so could pull from there.
videoFaceAnalysis = function(inputVideo, recordingStartDateTime, sampleWindow, facesCollectionID=NA) {
require(paws)
require(magick)
svc = rekognition()
recordingStartDateTime = as.POSIXct(recordingStartDateTime)
# This reads in the stills from the video. This would output one image every 60 seconds.
imagesFromVideo = image_read_video(inputVideo, fps=(1/sampleWindow))
# Create a directory structure to hold some temporary image files.
va_temp_dir = paste(dirname(inputVideo),"videoFaceAnalysis_temp", sep="/")
dir.create(va_temp_dir)
base_name = strsplit(basename(inputVideo), ".", fixed=T)[[1]][[1]]
img_temp_dir = paste(dirname(inputVideo),"videoFaceAnalysis_temp", base_name, sep="/")
dir.create(img_temp_dir)
# This now goes through each of the images that was extracted from the video. For each, it writes it to disk and gets the face details that are in the image (if there are any)
# Note: This is clunky and it would be better to just put them in the Amazon collection straight away. Will do that when have more time.
df.o = list()
inf = list()
for(videoCounter in 1:length(imagesFromVideo)) {
# This puts the timestamp on this clip
img_timestamp = recordingStartDateTime + (videoCounter-1)*sampleWindow
# Write the image to the temporary directory that was created
image_write(imagesFromVideo[videoCounter], paste(img_temp_dir,"/","img_",videoCounter,".png", sep=""), format="png")
# Get the information about the file (this is used later for face analysis)
inf[[videoCounter]] = image_info(imagesFromVideo[videoCounter])
# Detect faces in this frame
df.o[[videoCounter]] = svc$detect_faces(Image=list(Bytes=paste(img_temp_dir, "/", "img_",videoCounter,".png", sep="")), Attributes="ALL")
# Get the details of any faces detected in this frame
faces = df.o[[videoCounter]]$FaceDetails
# If there are no faces in the image, then create a blank results record, with just the image id
if(length(faces) == 0) {
res.line = matrix(nrow=1,ncol=23)
res.line[1,1] = paste(base_name, "-","img_", videoCounter, sep="")
res.line[1, 21] = img_timestamp
} else {
# Otherwise, if there are faces in the image, go through each face to get its info
# create a matrix to hold the info
res.line = matrix(nrow=length(faces), ncol=23)
# Loop through each face and analyze it
for(face.num in 1:length(faces)) {
fd = faces[[face.num]]
res.line[face.num,1] = paste(base_name, "-","img_", videoCounter, sep="")
res.line[face.num,2] = face.num
res.line[face.num,3] = fd$AgeRange$Low
res.line[face.num,4] = fd$AgeRange$High
res.line[face.num,5] = fd$Smile$Value
res.line[face.num,6] = fd$Eyeglasses$Value
res.line[face.num,7] = fd$Sunglasses$Value
res.line[face.num,8] = fd$Gender$Value
res.line[face.num,9] = fd$Beard$Value
res.line[face.num,10] = fd$Mustache$Value
res.line[face.num,11] = fd$EyesOpen$Value
res.line[face.num,12] = fd$MouthOpen$Value
# Make an emotions table for this image
for(e in fd$Emotions) {
if(e$Type == "CONFUSED") res.line[face.num,13] = e$Confidence
else if(e$Type == "CALM") res.line[face.num,14] = e$Confidence
else if(e$Type == "HAPPY") res.line[face.num,15] = e$Confidence
else if(e$Type == "DISGUSTED") res.line[face.num,16] = e$Confidence
else if(e$Type == "ANGRY") res.line[face.num,17] = e$Confidence
else if(e$Type == "FEAR") res.line[face.num,18] = e$Confidence
else if(e$Type == "SAD") res.line[face.num,19] = e$Confidence
else if(e$Type == "SURPRISED") res.line[face.num,20] = e$Confidence
}
res.line[face.num, 21] = img_timestamp
# if the user specified a face collection, go into it to see if the face has an identity
# Including the confidence value because it sometimes couldn't tell it was a face
# at low levels of confidence
if(!is.na(facesCollectionID) && fd$Confidence > 90) {
# Identify the coordinates of the face. Note that AWS returns percentage values of the total image size. This is
# why the image info object above is needed
box = fd$BoundingBox
image_width=inf[[videoCounter]]$width
image_height=inf[[videoCounter]]$height
x1 = box$Left*image_width
y1 = box$Top*image_height
x2 = x1 + box$Width*image_width
y2 = y1 + box$Height*image_height
# Crop out just this particular face out of the video
img.crop = image_crop(imagesFromVideo[videoCounter], paste(box$Width*image_width,"x",box$Height*image_height,"+",x1,"+",y1, sep=""))
img.crop = image_write(img.crop, path = NULL, format = "png")
# Search in a specified collection to see if we can label the identity of the face is in this crop
faceRec = svc$search_faces_by_image(CollectionId=facesCollectionID,Image=list(Bytes=img.crop), FaceMatchThreshold=70)
if(length(faceRec$FaceMatches) > 0) {
res.line[face.num, 22] = faceRec$FaceMatches[[1]]$Face$ExternalImageId
res.line[face.num, 23] = faceRec$FaceMatches[[1]]$Face$Confidence
} else {
res.line[face.num, 22] = "IDENTITY NOT RECOGNIZED"
}
}
}
}
if(videoCounter == 1) {
raw.res.out = res.line
} else {
raw.res.out = rbind(raw.res.out, res.line)
}
}
# Do some final formatting on the returned object
res.out = data.frame(raw.res.out, stringsAsFactors=F)
col.numeric = c(2:4, 13:20, 23)
col.boolean = c(5:7,9:12)
col.names = c("frame_id", "face_id", "age_low", "age_high", "smile", "eyeglasses", "sunglasses", "gender", "beard", "mustache", "eyesopen", "mouthopen", "confused", "calm", "happy", "disgusted", "angry", "fear", "sad", "surprised", "img_timestamp", "identified_person", "identification_confidence")
res.out[,col.numeric] = lapply(res.out[,col.numeric], as.numeric)
res.out[,col.boolean] = lapply(res.out[,col.boolean], as.logical)
res.out[,21] = as.POSIXct(as.numeric(res.out[,21]), origin="1970-01-01")
names(res.out) = col.names
res.out = res.out[, c(1,21,22,23, 2:20)]
return(res.out)
}
############################################################
# textConversationAnalysis Function
############################################################
# Conversation Analysis Function
# This function takes in the output of one of the other functions (either processZoomChat or processZoomTranscript) and produces # a set of conversation measures. I don't know conversation analysis, so this is just a rough cut of things I was
# curious about. If you are a conversation expert and want to contribute, let me know!
#
# Example Call: o = textConversationAnalysis(inputData=outputOfOtherFunctions, inputType="chat", sentiment=TRUE, speakerId = "user_name")
# INPUT ARGUMENTS:
# inputData The output from either the processZoomChat or processZoomTranscript functions
# inputType either "chat" or "transcript"
# speakerId The name of the variable in inputData that contains the unique identfier (e.g., "user_name")
# sentiment Boolean to indicate whether you want to output sentiment analysis metrics, as well.
# OUTPUT:
# this function outputs a list with two items. One is a set of measures aggregated for the overall file (either chat or transcript). The second is a set of measures aggregated to the level of the individual speaker. The function presumes that speakerId is a unique identifier and treats each value as the ID for a speaker.
# Note that any time measures in the output are represented as seconds.
# Most variable names are self-explanatory, but I'll highlight a few:
#### For the overall transcript output ####
# utterance_gap_x: This is the average number of seconds between one person's utterance and the next person's utterance
# utterance_gap_sd: The SD of the utterance gaps
# burstiness_raw: This is a measure of how concentrated (in time) utterances are. It is not adjusted for # of utterances
#### For the speaker-level transcript output ####
# utterance_gap_x: This is the average number of seconds (from the last utterance) that pass before this person makes an utterance
#### For the overall chat output ####
# message_gap_x: This is the average number of seconds between one person's message and the next person's message
# message_gap_sd: The SD of the message gaps
# burstiness_raw: This is a measure of how concentrated (in time) chat messages are. It is not adjusted for # of messages
#### For the speaker-level chat output ####
# message_gap_x: This is the average number of seconds (from the last message) that pass before this person sends a message
############################################################
textConversationAnalysis = function(inputData, inputType, speakerId, sentiment=FALSE) {
require(data.table)
########################################
# IF THE USER REQUESTED AN ANALYSIS OF A TRANSCRIPT FILE, DO THE FOLLOWING
########################################
if(inputType=="transcript") {
########################################
# Do the sentiment analysis if it was requested
########################################
if(sentiment==TRUE) {
inputData = sentiOut(inputData=inputData, idVar="utterance_id", textVar="utterance_message", languageCodeVar="utterance_language")
tab_denom = nrow(inputData[!is.na(inputData$sent_class), ])
utterance_positive_pct = nrow(inputData[inputData$sent_class=="POSITIVE", ])/tab_denom
utterance_neutral_pct = nrow(inputData[inputData$sent_class=="NEUTRAL", ])/tab_denom
utterance_negative_pct = nrow(inputData[inputData$sent_class=="NEGATIVE", ])/tab_denom
utterance_mixed_pct = nrow(inputData[inputData$sent_class=="MIXED", ])/tab_denom
utterance_mixed_x = mean(inputData$Mixed, na.rm=T)
utterance_neutral_x = mean(inputData$Neutral, na.rm=T)
utterance_negative_x = mean(inputData$Negative, na.rm=T)
utterance_positive_x = mean(inputData$Positive, na.rm=T)
utterance_mixed_sd = sd(inputData$Mixed, na.rm=T)
utterance_neutral_sd = sd(inputData$Neutral, na.rm=T)
utterance_negative_sd = sd(inputData$Negative, na.rm=T)
utterance_positive_sd = sd(inputData$Positive, na.rm=T)
sent.cols = cbind(utterance_positive_pct, utterance_positive_x, utterance_positive_sd, utterance_neutral_pct, utterance_neutral_x, utterance_neutral_sd, utterance_negative_pct, utterance_negative_x, utterance_negative_sd, utterance_mixed_pct, utterance_mixed_x, utterance_mixed_sd)
}
########################################
# Create a transcript-level output
########################################
# First, get some overall statistics - all time units are in seconds
total_recorded_time = as.numeric(difftime(max(inputData$utterance_end_time), min(inputData$utterance_start_time), units="secs"))
utterance_time_window_sum = sum(inputData$utterance_time_window)
silent_time_sum = total_recorded_time-utterance_time_sum
utterance_time_window_x = mean(inputData$utterance_time_window, na.rm=T)
utterance_time_window_sd = sd(inputData$utterance_time_window, na.rm=T)
num_unique_speakers = length(unique(inputData[,speakerId]))
num_utterances = nrow(inputData)
# Second get the information for burstiness, which is calculated as the CV of
# the gap between utterances (so concentration of speech)
# Figure out the gap from one utterance to the next
inputData$utterance_gap = NA
for(i in 2:nrow(inputData)) {
# start time of current utterance - end time of prior utterance (in seconds)
inputData[i, "utterance_gap"] = as.numeric(difftime(inputData[i, "utterance_start_time"], inputData[(i-1), "utterance_end_time"], units="secs"))
}
utterance_gap_x = mean(inputData$utterance_gap, na.rm=T)
utterance_gap_sd = sd(inputData$utterance_gap, na.rm=T)
burstiness_raw = (sd(inputData$utterance_gap, na.rm=T)-mean(inputData$utterance_gap, na.rm=T))/(sd(inputData$utterance_gap, na.rm=T)+mean(inputData$utterance_gap, na.rm=T))
transcript_out = cbind(total_recorded_time, num_utterances, num_unique_speakers, utterance_time_window_sum, silent_time_sum, utterance_time_window_x, utterance_time_window_sd, utterance_gap_x, utterance_gap_sd, burstiness_raw)
if(sentiment==TRUE) transcript_out = cbind(transcript_out, sent.cols)
########################################
# Create an individual-level output
# Note, the presumption is that user_name is something unique -- hopefully it is!
########################################
dt = data.table(inputData)
if(sentiment == TRUE) {
agg.dt = dt[,list(utterance_time_sum = sum(utterance_time_window, na.rm=T), num_utterances = .N, utterance_time_x = mean(utterance_time_window, na.rm=T), utterance_time_sd = sd(utterance_time_window, na.rm=T), utterance_gap_x = mean(utterance_gap, na.rm=T), utterance_gap_sd = sd(utterance_gap, na.rm=T),
utterance_positive_pct = sum(sent_class=="POSITIVE")/.N, utterance_positive_x = mean(Positive, na.rm=T), utterance_positive_sd = sd(Positive, na.rm=T),
utterance_negative_pct = sum(sent_class=="NEGATIVE")/.N, utterance_negative_x = mean(Negative, na.rm=T), utterance_negative_sd = sd(Negative, na.rm=T),
utterance_neutral_pct = sum(sent_class=="NEUTRAL")/.N, utterance_neautral_x = mean(Neutral, na.rm=T), utterance_neutral_sd = sd(Neutral, na.rm=T),
utterance_mixed_pct = sum(sent_class=="MIXED")/.N, utterance_mixed_x = mean(Mixed, na.rm=T), utterance_mixed_sd = sd(Mixed, na.rm=T)
), by=list(get(speakerId))]
names(agg.dt)[1] = speakerId
} else {
agg.dt = dt[,list(utterance_time_sum = sum(utterance_time_window, na.rm=T), num_utterances = .N, utterance_time_x = mean(utterance_time_window, na.rm=T), utterance_time_sd = sd(utterance_time_window, na.rm=T), utterance_gap_x = mean(utterance_gap, na.rm=T), utterance_gap_sd = sd(utterance_gap, na.rm=T)), by=list(get(speakerId))]
names(agg.dt)[1] = speakerId
}
agg.out = data.frame(agg.dt)
res.out = list("TRANSCRIPT-LEVEL" = transcript_out, "SPEAKER-LEVEL" = agg.out)
########################################
# IF THE USER REQUESTED AN ANALYSIS OF A CHAT FILE, DO THE FOLLOWING
########################################
} else if(inputType=="chat") {
########################################
# Do the sentiment analysis if it was requested
########################################
if(sentiment==TRUE) {
inputData = sentiOut(inputData=inputData, idVar="message_id", textVar="message", languageCodeVar="message_language")
tab_denom = nrow(inputData[!is.na(inputData$sent_class), ])
message_positive_pct = nrow(inputData[inputData$sent_class=="POSITIVE", ])/tab_denom
message_neutral_pct = nrow(inputData[inputData$sent_class=="NEUTRAL", ])/tab_denom
message_negative_pct = nrow(inputData[inputData$sent_class=="NEGATIVE", ])/tab_denom
message_mixed_pct = nrow(inputData[inputData$sent_class=="MIXED", ])/tab_denom
message_mixed_x = mean(inputData$Mixed, na.rm=T)
message_neutral_x = mean(inputData$Neutral, na.rm=T)
message_negative_x = mean(inputData$Negative, na.rm=T)
message_positive_x = mean(inputData$Positive, na.rm=T)
message_mixed_sd = sd(inputData$Mixed, na.rm=T)
message_neutral_sd = sd(inputData$Neutral, na.rm=T)
message_negative_sd = sd(inputData$Negative, na.rm=T)
message_positive_sd = sd(inputData$Positive, na.rm=T)
sent.cols = cbind(message_positive_pct, message_positive_x, message_positive_sd, message_neutral_pct, message_neutral_x, message_neutral_sd, message_negative_pct, message_negative_x, message_negative_sd, message_mixed_pct, message_mixed_x, message_mixed_sd)
}
########################################
# Create a chat-level output
########################################
inputData$message_numchars = nchar(inputData$message)
# First, get some overall statistics - all time units are in seconds
total_recorded_time = as.numeric(difftime(max(inputData$message_time), min(inputData$message_time), units="secs"))
message_numchars_sum = sum(inputData$message_numchars)
message_numchars_x = mean(inputData$message_numchars)
message_numchars_sd = sd(inputData$message_numchars)
num_unique_messagers = length(unique(inputData[,speakerId]))
num_messages = nrow(inputData)
# Second get the information for burstiness, which is calculated as the CV of
# the gap between messages (so concentration of speech)
# Figure out the gap from one message to the next
inputData$message_gap = NA
for(i in 2:nrow(inputData)) {
# start time of current utterance - end time of prior utterance (in seconds)
inputData[i, "message_gap"] = as.numeric(difftime(inputData[i, "message_time"], inputData[(i-1), "message_time"], units="secs"))
}
message_gap_x = mean(inputData$message_gap, na.rm=T)
message_gap_sd = sd(inputData$message_gap, na.rm=T)
burstiness_raw = (sd(inputData$message_gap, na.rm=T)-mean(inputData$message_gap, na.rm=T))/(sd(inputData$message_gap, na.rm=T)+mean(inputData$message_gap, na.rm=T))
chat_out = cbind(total_recorded_time, num_messages, message_numchars_sum, num_unique_messagers, message_gap_x, message_gap_sd, burstiness_raw)
if(sentiment==TRUE) chat_out = cbind(chat_out, sent.cols)
########################################
# Create an individual-level output
# Note, the presumption is that user_name is something unique -- hopefully it is!
########################################
dt = data.table(inputData)
if(sentiment == TRUE) {
agg.dt = dt[,list(message_numchars_sum = sum(message_numchars, na.rm=T), num_messages = .N, message_numchars_x = mean(message_numchars), message_numchars_sd = sd(message_numchars), message_gap_x = mean(message_gap, na.rm=T), message_gap_sd = sd(message_gap, na.rm=T),
message_positive_pct = sum(sent_class=="POSITIVE")/.N, message_positive_x = mean(Positive, na.rm=T), message_positive_sd = sd(Positive, na.rm=T),
message_negative_pct = sum(sent_class=="NEGATIVE")/.N, message_negative_x = mean(Negative, na.rm=T), message_negative_sd = sd(Negative, na.rm=T),
message_neutral_pct = sum(sent_class=="NEUTRAL")/.N, message_neautral_x = mean(Neutral, na.rm=T), message_neutral_sd = sd(Neutral, na.rm=T),
message_mixed_pct = sum(sent_class=="MIXED")/.N, message_mixed_x = mean(Mixed, na.rm=T), message_mixed_sd = sd(Mixed, na.rm=T)
), by=list(get(speakerId))]
names(agg.dt)[1] = speakerId
} else {
agg.dt = dt[,list(message_numchars_sum = sum(message_numchars, na.rm=T), num_messages = .N, message_numchars_x = mean(message_numchars), message_numchars_sd = sd(message_numchars), message_gap_x = mean(message_gap, na.rm=T), message_gap_sd = sd(message_gap, na.rm=T)
), by=list(get(speakerId))]
names(agg.dt)[1] = speakerId
}
agg.out = data.frame(agg.dt)
res.out = list("CHAT-LEVEL" = chat_out, "USER-LEVEL" = agg.out)
}
}
# Author: Andrew Knight (http://apknight.org)
# Last Update: 2020-04-14 13:00 US CDT
# Update Note: Added the textConversationAnalysis function
# I created this as a way to help people do social science research through web-based meetings (i.e., Zoom).
# It's still a work in progress, but this is a start. If you would like to use it or help build it,
# please reach out!
############################################################
# OVERVIEW OF FUNCTIONS
############################################################
# This script contains functions to use for analyzing recorded Zoom sessions
# This is a work in progress and more are coming.
# processZoomChat Parses the downloaded chat file from a recorded Zoom session
# processZoomTranscript Parses the downloaded transcript from a recorded Zoom session
# sentiOut Conducts a sentiment analysis on either the Chat or Transcript
# videoFaceAnalysis Analyzes the video from a Zoom session and outputs face/emotion measures
# textConversationAnalysis Analyzes either chat or transcript and outputs conversation metrics
# Note you will require the following packages to run these:
# reshape2
# stringr
# paws
# magick
# data.table
# You will also require an aws account with privileges for rekognition and comprehend to use the
# text analysis and video analysis. If you don't know how to do this, please:
# Search online for (a) setting up AWS account; (b) setting up paws. I found the following useful:
# https://github.com/paws-r/paws/blob/master/docs/credentials.md
# If, after you try you are still struggling, I can give guidance on this if useful--just contact me.
############################################################
# processZoomChat Function
############################################################
# Zoom Chat File Processing
# This function parses the data from the chatfile that is downloaded from the Zoom website.
# NOTE: This is the file that accompanies a recording. This is not the file
# that you download directly within the window. It is also not the one that is
# saved locally on your computer. This is the file that you can access after a session
# if you record in the cloud.
# example call: ch.out = processZoomChat(fname="~/Desktop/chat.txt", sessionStartDateTime="2020-04-01 17:56:34", languageCode="en")
# INPUT ARGUMENTS:
# fname: the path to the local file where the chat file (txt) is saved.
# sessionStartDateTime: the time that the actual session was launched. Format is YYYY-MM-DD HH:MM:SS
# languageCode: the code for the language (e.g., en)
# OUTPUT:
# message_id: an incremented numeric identifier for a message in the chat
# message_time: a timestamp for the message, based on the start of the Zoom session
# user_name: the name attached to the message
# message: the text of the message
# message_language: the language code for the chat
processZoomChat = function(fname, sessionStartDateTime, languageCode) {
require(reshape2)
require(stringr)
ch = read.delim(fname, sep="\t", stringsAsFactors=F, header=F, col.names=c("message_increment", "user_name", "message"))
####################################
# First thing do to is to create a message_time variable
# This is user-supplied and could come from the usermeeting report that can be downloaded
sessionStartDateTime = as.POSIXct(sessionStartDateTime)
# This is the value embedded in the chat record. It is an HH:MM:SS delta from the start of the session
# I'm doing a crude parse to just get the total number of seconds that the delta is. This is then
# used as the increment from the sessionStartDateTime
ch$inc_hours = as.numeric(substr(ch$message_increment,1,2))
ch$inc_mins = as.numeric(substr(ch$message_increment,4,5))
ch$inc_secs = as.numeric(substr(ch$message_increment,7,8))
ch$inc_total_secs = ch$inc_hours*60*60 + ch$inc_mins*60 + ch$inc_secs
ch$message_time = sessionStartDateTime + ch$inc_total_secs
####################################
# Chat transcripts do not handle soft returns well (i.e., if the same person uses a soft line break
# for multiple lines in a single message that is submitted to the system).
# This is a crude way to identify them based on someone having an invalid message time.
# For now, will assign that text to the last marked user name in the dataset,
# pasting the messages together into a single line (separated by a space. )
# Create a flag to mark erroneous records based on the message time variable. This should be made stronger
# and cleaner eventually
ch$flag = is.na(as.integer(substr(ch$message_increment,1,1))) + (nchar(ch$message_increment) != 8)
# Assign the value in the message_increment variable to the message variable. This is because
# the parsing of the file is fucked up when there are soft returns in someone's chat message
ch$message = ifelse(ch$flag > 0, ch$message_increment, ch$message)
# Go through the records from the bottom up to paste the message on the one it
# should be part of
for(i in nrow(ch):1) {
if(ch[i,"flag"] > 0) {
ch[(i-1), "message"] = paste(ch[(i-1), "message"], ch[i, "message"], sep=" ")
}
}
# now drop the unnecessary records
ch = ch[ch$flag == 0, ]
# get rid of whitespace at the beginning and end
ch$message = str_trim(ch$message, "both")
# Add a language variable, which is user-supplied for now
ch$message_language = languageCode
# Add a simple numeric incrementing identifier for the messages that people submitted
ch$message_id = 1:nrow(ch)
# Get rid of the superfluous colon at the end of the usernames
ch$user_name = substr(ch$user_name, 1, nchar(ch$user_name)-1)
# Clean up the ordering of variables that are returned
ch = ch[,c("message_id", "message_time", "user_name", "message", "message_language")]
return(ch)
}
############################################################
# processZoomTranscript Function
############################################################
# Zoom Recording Transcript File Processing
# This function parses the data from the transcript file (.vtt) that is downloaded from the Zoom website.
# NOTE: This is the file that accompanies a recording to the cloud.
# example call: tr.out = processZoomTranscript(fname="~/Desktop/transcript.vtt", recordingStartTime="2020-04-01 17:56:34", languageCode="en")
# INPUT:
# fname: the path to the local file where the transcript file (vtt) is saved.
# recordingStartDateTime: the time that the recording was launched. Format is YYYY-MM-DD HH:MM:SS
# languageCode: the code for the language (e.g., en)
# Note: I plan to fix at a later point in time the timing issue. Specifically, it is not clear
# where in Zoom's system I can get the actual time that the recording was started. This
# is a problem for linking the transcript file up with the chat file.
# One workaround for now (for research) would be to set recordings to auto-start. This is not ideal, though.
# we should be able to know when the recording was started. It is embedded in the video, so could pull from there.
# OUTPUT:
# utterance_id: an incremented numeric identifier for a marked speech utterance
# utterance_start_time: the timestamp for the start of the utterance
# utterance_end_time: the timestamp for the end of the utterance
# utterance_time_window: the number of seconds that the utterance took
# user_name: the name attached to the utterance
# utterance_message: the text of the utterance
# utterance_language: the language code for the transcript
processZoomTranscript = function(fname, recordingStartDateTime, languageCode) {
library(reshape2)
require(stringr)
# Parse the transcript file -- vtt is a structured format.
f = readLines(fname)
# there are three main pieces of data for each marked "utterance" - an id, a window of time, and the text
utterance_id = as.integer(f[seq(3,length(f), 4)])
utterance_window = f[seq(4,length(f), 4)]
utterance_text = f[seq(5,length(f), 4)]
# Parse the time window into two separate elements
utterance_start_time = unlist(strsplit(utterance_window, " --> "))[seq(1, length(utterance_window)*2, 2)]
utterance_end_time = unlist(strsplit(utterance_window, " --> "))[seq(2, length(utterance_window)*2, 2)]
# Just a little function to do this X 2
timeCalc = function(startTime, incTime) {
inc_hours = as.numeric(substr(incTime,1,2))
inc_mins = as.numeric(substr(incTime,4,5))
inc_secs = as.numeric(substr(incTime,7,12))
inc_total_secs = inc_hours*60*60 + inc_mins*60 + inc_secs
correct_time = startTime + inc_total_secs
}
# Now turn these into actual datetime values
recordingStartDateTime = as.POSIXct(recordingStartDateTime)
utterance_start_time = timeCalc(recordingStartDateTime, utterance_start_time)
utterance_end_time = timeCalc(recordingStartDateTime, utterance_end_time)
# Create a time window (in seconds) for the utterances -- how long is each in seconds
utterance_time_window = as.numeric(difftime(utterance_end_time, utterance_start_time, units="secs"))
# Parse the utterance message itself
utterance_message = substring(utterance_text, regexpr("[:]", utterance_text)+2)
# Get the user name that spoke the text
user_name = substr(utterance_text, 1, regexpr("[:]", utterance_text)-1)
# Prepare the output file
res.out = data.frame(utterance_id, utterance_start_time, utterance_end_time, utterance_time_window, user_name, utterance_message, stringsAsFactors=F)
# Add the language code
res.out$utterance_language = languageCode
return(res.out)
}
############################################################
# sentiOut Function
############################################################
# Text-based sentiment analysis function
# This function takes in the output of the chat and transcript functions. It then
# conducts a sentiment analysis on an identified chunk of text
# and returns the values.
# To use this function, you must have an aws account that with privileges for the comprehend service
# However you authenticate for AWS, you should do so before running the function.
# example call: sent.out = sentiOut(inputData=tr.out, idVar = "utterance_id", textVar = "utterance_message", languageCodeVar = "utterance_language")
# INPUT:
# inputData: the input data frame
# idVar: the name of the id variable for the text
# textVar: the name of the variable with the text in it
# languageCodeVar: the variable containing the language code for each text chunk
# OUTPUT: This function returns the inputData plus the following variables
# sent_class: the text-based sentiment classification of the message
# Mixed: the confidence level for the text being mixed sentiment
# Negative: the confidence level for the text being negative sentiment
# Neutral: the confidence level for the text being neutral sentiment
# Positive: the confidence level for the text being positive sentiment
# Note: This function currently does this in a brute force way. In the future, I will
# build this so that it batches chunks of text to run, rather than looping through.
sentiOut = function(inputData, idVar, textVar, languageCodeVar){
require(paws)
require(reshape2)
# Identify the AWS service comprehend:
# AS STATED ABOVE--YOU MUST HAVE AN AUTHENTICATED ACCOUNT WITH THE RIGHT PRIVILIGES
svc = comprehend()
# Loop through each record of the inputData
for(i in 1:nrow(inputData)) {
# Run the sentiment detection function from AWS Comprehend on this chunk of text
sent = svc$detect_sentiment(Text = inputData[i,textVar], LanguageCode=inputData[i,languageCodeVar])
# Create a simple
res.line = cbind(inputData[i,idVar],unlist(sent$SentimentScore), sent$Sentiment)
if(i == 1) {
res.out = res.line
} else {
res.out = rbind(res.out, res.line)
}
}
# Now, clean up the output so that it comes as a dataframe
d.res = data.frame(res.out, stringsAsFactors=F)
names(d.res) = c(idVar, "sent_value", "sent_class")
d.res$sent_type = unlist(lapply(strsplit(row.names(d.res), '[.]'), '[[',1))
d.res.melt = reshape2::melt(d.res, idVars=c(idVar, sent_class, "sent_type"), variable.name="sent_variable", value.name="sent_value")
d.res.wide = reshape2::dcast(d.res.melt, get(idVar) + sent_class ~ sent_type, value.var="sent_value")
names(d.res.wide)[1] = idVar
d.res.wide[,c("Mixed", "Negative", "Neutral", "Positive")] = lapply(d.res.wide[,c("Mixed", "Negative", "Neutral", "Positive")], as.numeric)
d.mrg = merge(inputData, d.res.wide, by=idVar, all.x=T)
return(d.mrg)
}
############################################################
# videoFaceAnalysis Function
############################################################
# Video-based sentiment analysis function
# This function takes in a video file and produces measures based on the faces in the
# video. Note that this is a very crude way of doing this. It uses the image detection
# capabilities in AWS rekognition to cut the video up in to a sample of frames, then
# analyzes those frames. rekognition has a video analysis feature that I'll incorporate
# later.
# For best results with recorded Zoom sessions:
# I would recommend going into your settings for recordings and choosing to
# record active speaker, gallery view, and shared screen separately.
# Make sure to choose to record the gallery view so that you get
# all of the faces in a single video feed. You also might want to choose "Optimize the recording..."
# To use this function, you must have an aws account that with privileges for the rekognition service
# However you authenticate for AWS, you should do so before running the function.
# example call: vid.out = videoFacesAnalysis(inputData="~/Desktop/sample_video.mp4", recordingStartTime="2020-04-01 17:56:34", sampleWindow=20, facesCollectionID="class15-r")
# INPUT ARGUMENTS:
# inputVideo: the input video file
# recordingStartDateTime: the name of the id variable for the text
# sampleWindow: the number of seconds in between each sample of the recording
# facesCollectionID: Not necessary: Name of an S3 collection if you want to ID specific people
# OUTPUT:
# frame_id an identifier for the frame of the video used for this record
# img_timestamp the timestamp of the image from the video (see note below re: recording)
# identified_person the name of the person identified in the frame, if a collection is given
# identification_confidence the confidence level for the identity (first one)
# face_id an identifier for the face in the frame
# age_low low boundary for estimated age
# age_high high boundary for estimated age
# smile boolean - does the face have smile
# eyeglasses boolean - does the face have eyeglasses
# sunglasses boolean - does the face have sunglasses
# gender gender of face
# beard boolean - does the face have beard
# mustache boolean - does the face have mustache
# eyesopen boolean - does the face have eyes open
# mouthopen boolean - does the face have mouth open
# confused confidence level for the face showing confused
# calm confidence level for the face showing calm
# happy confidence level for the face showing happy
# disgusted confidence level for the face showing disgusted
# angry confidence level for the face showing angry
# fear confidence level for the face showing fear
# sad confidence level for the face showing sad
# surprised confidence level for the face showing surprised
# Note: This function currently es things in a brute force way. I'll refine this so that
# things are processed in batch, rather than in so many gross loops.
# Note: Same as with transcripts: I plan to fix at a later point in time the timing issue. Specifically, it is not clear where in Zoom's system I can get the actual time that the recording was started. This
# is a problem for linking the transcript file up with the chat file.
# One workaround for now (for research) would be to set recordings to auto-start. This is not ideal, though.
# we should be able to know when the recording was started. It is embedded in the video, so could pull from there.
videoFaceAnalysis = function(inputVideo, recordingStartDateTime, sampleWindow, facesCollectionID=NA) {
require(paws)
require(magick)
svc = rekognition()
recordingStartDateTime = as.POSIXct(recordingStartDateTime)
# This reads in the stills from the video. This would output one image every 60 seconds.
imagesFromVideo = image_read_video(inputVideo, fps=(1/sampleWindow))
# Create a directory structure to hold some temporary image files.
va_temp_dir = paste(dirname(inputVideo),"videoFaceAnalysis_temp", sep="/")
dir.create(va_temp_dir)
base_name = strsplit(basename(inputVideo), ".", fixed=T)[[1]][[1]]
img_temp_dir = paste(dirname(inputVideo),"videoFaceAnalysis_temp", base_name, sep="/")
dir.create(img_temp_dir)
# This now goes through each of the images that was extracted from the video. For each, it writes it to disk and gets the face details that are in the image (if there are any)
# Note: This is clunky and it would be better to just put them in the Amazon collection straight away. Will do that when have more time.
df.o = list()
inf = list()
for(videoCounter in 1:length(imagesFromVideo)) {
# This puts the timestamp on this clip
img_timestamp = recordingStartDateTime + (videoCounter-1)*sampleWindow
# Write the image to the temporary directory that was created
image_write(imagesFromVideo[videoCounter], paste(img_temp_dir,"/","img_",videoCounter,".png", sep=""), format="png")
# Get the information about the file (this is used later for face analysis)
inf[[videoCounter]] = image_info(imagesFromVideo[videoCounter])
# Detect faces in this frame
df.o[[videoCounter]] = svc$detect_faces(Image=list(Bytes=paste(img_temp_dir, "/", "img_",videoCounter,".png", sep="")), Attributes="ALL")
# Get the details of any faces detected in this frame
faces = df.o[[videoCounter]]$FaceDetails
# If there are no faces in the image, then create a blank results record, with just the image id
if(length(faces) == 0) {
res.line = matrix(nrow=1,ncol=23)
res.line[1,1] = paste(base_name, "-","img_", videoCounter, sep="")
res.line[1, 21] = img_timestamp
} else {
# Otherwise, if there are faces in the image, go through each face to get its info
# create a matrix to hold the info
res.line = matrix(nrow=length(faces), ncol=23)
# Loop through each face and analyze it
for(face.num in 1:length(faces)) {
fd = faces[[face.num]]
res.line[face.num,1] = paste(base_name, "-","img_", videoCounter, sep="")
res.line[face.num,2] = face.num
res.line[face.num,3] = fd$AgeRange$Low
res.line[face.num,4] = fd$AgeRange$High
res.line[face.num,5] = fd$Smile$Value
res.line[face.num,6] = fd$Eyeglasses$Value
res.line[face.num,7] = fd$Sunglasses$Value
res.line[face.num,8] = fd$Gender$Value
res.line[face.num,9] = fd$Beard$Value
res.line[face.num,10] = fd$Mustache$Value
res.line[face.num,11] = fd$EyesOpen$Value
res.line[face.num,12] = fd$MouthOpen$Value
# Make an emotions table for this image
for(e in fd$Emotions) {
if(e$Type == "CONFUSED") res.line[face.num,13] = e$Confidence
else if(e$Type == "CALM") res.line[face.num,14] = e$Confidence
else if(e$Type == "HAPPY") res.line[face.num,15] = e$Confidence
else if(e$Type == "DISGUSTED") res.line[face.num,16] = e$Confidence
else if(e$Type == "ANGRY") res.line[face.num,17] = e$Confidence
else if(e$Type == "FEAR") res.line[face.num,18] = e$Confidence
else if(e$Type == "SAD") res.line[face.num,19] = e$Confidence
else if(e$Type == "SURPRISED") res.line[face.num,20] = e$Confidence
}
res.line[face.num, 21] = img_timestamp
# if the user specified a face collection, go into it to see if the face has an identity
# Including the confidence value because it sometimes couldn't tell it was a face
# at low levels of confidence
if(!is.na(facesCollectionID) && fd$Confidence > 90) {
# Identify the coordinates of the face. Note that AWS returns percentage values of the total image size. This is
# why the image info object above is needed
box = fd$BoundingBox
image_width=inf[[videoCounter]]$width
image_height=inf[[videoCounter]]$height
x1 = box$Left*image_width
y1 = box$Top*image_height
x2 = x1 + box$Width*image_width
y2 = y1 + box$Height*image_height
# Crop out just this particular face out of the video
img.crop = image_crop(imagesFromVideo[videoCounter], paste(box$Width*image_width,"x",box$Height*image_height,"+",x1,"+",y1, sep=""))
img.crop = image_write(img.crop, path = NULL, format = "png")
# Search in a specified collection to see if we can label the identity of the face is in this crop
faceRec = svc$search_faces_by_image(CollectionId=facesCollectionID,Image=list(Bytes=img.crop), FaceMatchThreshold=70)
if(length(faceRec$FaceMatches) > 0) {
res.line[face.num, 22] = faceRec$FaceMatches[[1]]$Face$ExternalImageId
res.line[face.num, 23] = faceRec$FaceMatches[[1]]$Face$Confidence
} else {
res.line[face.num, 22] = "IDENTITY NOT RECOGNIZED"
}
}
}
}
if(videoCounter == 1) {
raw.res.out = res.line
} else {
raw.res.out = rbind(raw.res.out, res.line)
}
}
# Do some final formatting on the returned object
res.out = data.frame(raw.res.out, stringsAsFactors=F)
col.numeric = c(2:4, 13:20, 23)
col.boolean = c(5:7,9:12)
col.names = c("frame_id", "face_id", "age_low", "age_high", "smile", "eyeglasses", "sunglasses", "gender", "beard", "mustache", "eyesopen", "mouthopen", "confused", "calm", "happy", "disgusted", "angry", "fear", "sad", "surprised", "img_timestamp", "identified_person", "identification_confidence")
res.out[,col.numeric] = lapply(res.out[,col.numeric], as.numeric)
res.out[,col.boolean] = lapply(res.out[,col.boolean], as.logical)
res.out[,21] = as.POSIXct(as.numeric(res.out[,21]), origin="1970-01-01")
names(res.out) = col.names
res.out = res.out[, c(1,21,22,23, 2:20)]
return(res.out)
}
############################################################
# textConversationAnalysis Function
############################################################
# Conversation Analysis Function
# This function takes in the output of one of the other functions (either processZoomChat or processZoomTranscript) and produces # a set of conversation measures. I don't know conversation analysis, so this is just a rough cut of things I was
# curious about. If you are a conversation expert and want to contribute, let me know!
#
# Example Call: o = textConversationAnalysis(inputData=outputOfOtherFunctions, inputType="chat", sentiment=TRUE, speakerId = "user_name")
# INPUT ARGUMENTS:
# inputData The output from either the processZoomChat or processZoomTranscript functions
# inputType either "chat" or "transcript"
# speakerId The name of the variable in inputData that contains the unique identfier (e.g., "user_name")
# sentiment Boolean to indicate whether you want to output sentiment analysis metrics, as well.
# OUTPUT:
# this function outputs a list with two items. One is a set of measures aggregated for the overall file (either chat or transcript). The second is a set of measures aggregated to the level of the individual speaker. The function presumes that speakerId is a unique identifier and treats each value as the ID for a speaker.
# Note that any time measures in the output are represented as seconds.
# Most variable names are self-explanatory, but I'll highlight a few:
#### For the overall transcript output ####
# utterance_gap_x: This is the average number of seconds between one person's utterance and the next person's utterance
# utterance_gap_sd: The SD of the utterance gaps
# burstiness_raw: This is a measure of how concentrated (in time) utterances are. It is not adjusted for # of utterances
#### For the speaker-level transcript output ####
# utterance_gap_x: This is the average number of seconds (from the last utterance) that pass before this person makes an utterance
#### For the overall chat output ####
# message_gap_x: This is the average number of seconds between one person's message and the next person's message
# message_gap_sd: The SD of the message gaps
# burstiness_raw: This is a measure of how concentrated (in time) chat messages are. It is not adjusted for # of messages
#### For the speaker-level chat output ####
# message_gap_x: This is the average number of seconds (from the last message) that pass before this person sends a message
############################################################
textConversationAnalysis = function(inputData, inputType, speakerId, sentiment=FALSE) {
require(data.table)
########################################
# IF THE USER REQUESTED AN ANALYSIS OF A TRANSCRIPT FILE, DO THE FOLLOWING
########################################
if(inputType=="transcript") {
########################################
# Do the sentiment analysis if it was requested
########################################
if(sentiment==TRUE) {
inputData = sentiOut(inputData=inputData, idVar="utterance_id", textVar="utterance_message", languageCodeVar="utterance_language")
tab_denom = nrow(inputData[!is.na(inputData$sent_class), ])
utterance_positive_pct = nrow(inputData[inputData$sent_class=="POSITIVE", ])/tab_denom
utterance_neutral_pct = nrow(inputData[inputData$sent_class=="NEUTRAL", ])/tab_denom
utterance_negative_pct = nrow(inputData[inputData$sent_class=="NEGATIVE", ])/tab_denom
utterance_mixed_pct = nrow(inputData[inputData$sent_class=="MIXED", ])/tab_denom
utterance_mixed_x = mean(inputData$Mixed, na.rm=T)
utterance_neutral_x = mean(inputData$Neutral, na.rm=T)
utterance_negative_x = mean(inputData$Negative, na.rm=T)
utterance_positive_x = mean(inputData$Positive, na.rm=T)
utterance_mixed_sd = sd(inputData$Mixed, na.rm=T)
utterance_neutral_sd = sd(inputData$Neutral, na.rm=T)
utterance_negative_sd = sd(inputData$Negative, na.rm=T)
utterance_positive_sd = sd(inputData$Positive, na.rm=T)
sent.cols = cbind(utterance_positive_pct, utterance_positive_x, utterance_positive_sd, utterance_neutral_pct, utterance_neutral_x, utterance_neutral_sd, utterance_negative_pct, utterance_negative_x, utterance_negative_sd, utterance_mixed_pct, utterance_mixed_x, utterance_mixed_sd)
}
########################################
# Create a transcript-level output
########################################
# First, get some overall statistics - all time units are in seconds
total_recorded_time = as.numeric(difftime(max(inputData$utterance_end_time), min(inputData$utterance_start_time), units="secs"))
utterance_time_window_sum = sum(inputData$utterance_time_window)
silent_time_sum = total_recorded_time-utterance_time_sum
utterance_time_window_x = mean(inputData$utterance_time_window, na.rm=T)
utterance_time_window_sd = sd(inputData$utterance_time_window, na.rm=T)
num_unique_speakers = length(unique(inputData[,speakerId]))
num_utterances = nrow(inputData)
# Second get the information for burstiness, which is calculated as the CV of
# the gap between utterances (so concentration of speech)
# Figure out the gap from one utterance to the next
inputData$utterance_gap = NA
for(i in 2:nrow(inputData)) {
# start time of current utterance - end time of prior utterance (in seconds)
inputData[i, "utterance_gap"] = as.numeric(difftime(inputData[i, "utterance_start_time"], inputData[(i-1), "utterance_end_time"], units="secs"))
}
utterance_gap_x = mean(inputData$utterance_gap, na.rm=T)
utterance_gap_sd = sd(inputData$utterance_gap, na.rm=T)
burstiness_raw = (sd(inputData$utterance_gap, na.rm=T)-mean(inputData$utterance_gap, na.rm=T))/(sd(inputData$utterance_gap, na.rm=T)+mean(inputData$utterance_gap, na.rm=T))
transcript_out = cbind(total_recorded_time, num_utterances, num_unique_speakers, utterance_time_window_sum, silent_time_sum, utterance_time_window_x, utterance_time_window_sd, utterance_gap_x, utterance_gap_sd, burstiness_raw)
if(sentiment==TRUE) transcript_out = cbind(transcript_out, sent.cols)
########################################
# Create an individual-level output
# Note, the presumption is that user_name is something unique -- hopefully it is!
########################################
dt = data.table(inputData)
if(sentiment == TRUE) {
agg.dt = dt[,list(utterance_time_sum = sum(utterance_time_window, na.rm=T), num_utterances = .N, utterance_time_x = mean(utterance_time_window, na.rm=T), utterance_time_sd = sd(utterance_time_window, na.rm=T), utterance_gap_x = mean(utterance_gap, na.rm=T), utterance_gap_sd = sd(utterance_gap, na.rm=T),
utterance_positive_pct = sum(sent_class=="POSITIVE")/.N, utterance_positive_x = mean(Positive, na.rm=T), utterance_positive_sd = sd(Positive, na.rm=T),
utterance_negative_pct = sum(sent_class=="NEGATIVE")/.N, utterance_negative_x = mean(Negative, na.rm=T), utterance_negative_sd = sd(Negative, na.rm=T),
utterance_neutral_pct = sum(sent_class=="NEUTRAL")/.N, utterance_neautral_x = mean(Neutral, na.rm=T), utterance_neutral_sd = sd(Neutral, na.rm=T),
utterance_mixed_pct = sum(sent_class=="MIXED")/.N, utterance_mixed_x = mean(Mixed, na.rm=T), utterance_mixed_sd = sd(Mixed, na.rm=T)
), by=list(get(speakerId))]
names(agg.dt)[1] = speakerId
} else {
agg.dt = dt[,list(utterance_time_sum = sum(utterance_time_window, na.rm=T), num_utterances = .N, utterance_time_x = mean(utterance_time_window, na.rm=T), utterance_time_sd = sd(utterance_time_window, na.rm=T), utterance_gap_x = mean(utterance_gap, na.rm=T), utterance_gap_sd = sd(utterance_gap, na.rm=T)), by=list(get(speakerId))]
names(agg.dt)[1] = speakerId
}
agg.out = data.frame(agg.dt)
res.out = list("TRANSCRIPT-LEVEL" = transcript_out, "SPEAKER-LEVEL" = agg.out)
########################################
# IF THE USER REQUESTED AN ANALYSIS OF A CHAT FILE, DO THE FOLLOWING
########################################
} else if(inputType=="chat") {
########################################
# Do the sentiment analysis if it was requested
########################################
if(sentiment==TRUE) {
inputData = sentiOut(inputData=inputData, idVar="message_id", textVar="message", languageCodeVar="message_language")
tab_denom = nrow(inputData[!is.na(inputData$sent_class), ])
message_positive_pct = nrow(inputData[inputData$sent_class=="POSITIVE", ])/tab_denom
message_neutral_pct = nrow(inputData[inputData$sent_class=="NEUTRAL", ])/tab_denom
message_negative_pct = nrow(inputData[inputData$sent_class=="NEGATIVE", ])/tab_denom
message_mixed_pct = nrow(inputData[inputData$sent_class=="MIXED", ])/tab_denom
message_mixed_x = mean(inputData$Mixed, na.rm=T)
message_neutral_x = mean(inputData$Neutral, na.rm=T)
message_negative_x = mean(inputData$Negative, na.rm=T)
message_positive_x = mean(inputData$Positive, na.rm=T)
message_mixed_sd = sd(inputData$Mixed, na.rm=T)
message_neutral_sd = sd(inputData$Neutral, na.rm=T)
message_negative_sd = sd(inputData$Negative, na.rm=T)
message_positive_sd = sd(inputData$Positive, na.rm=T)
sent.cols = cbind(message_positive_pct, message_positive_x, message_positive_sd, message_neutral_pct, message_neutral_x, message_neutral_sd, message_negative_pct, message_negative_x, message_negative_sd, message_mixed_pct, message_mixed_x, message_mixed_sd)
}
########################################
# Create a chat-level output
########################################
inputData$message_numchars = nchar(inputData$message)
# First, get some overall statistics - all time units are in seconds
total_recorded_time = as.numeric(difftime(max(inputData$message_time), min(inputData$message_time), units="secs"))
message_numchars_sum = sum(inputData$message_numchars)
message_numchars_x = mean(inputData$message_numchars)
message_numchars_sd = sd(inputData$message_numchars)
num_unique_messagers = length(unique(inputData[,speakerId]))
num_messages = nrow(inputData)
# Second get the information for burstiness, which is calculated as the CV of
# the gap between messages (so concentration of speech)
# Figure out the gap from one message to the next
inputData$message_gap = NA
for(i in 2:nrow(inputData)) {
# start time of current utterance - end time of prior utterance (in seconds)
inputData[i, "message_gap"] = as.numeric(difftime(inputData[i, "message_time"], inputData[(i-1), "message_time"], units="secs"))
}
message_gap_x = mean(inputData$message_gap, na.rm=T)
message_gap_sd = sd(inputData$message_gap, na.rm=T)
burstiness_raw = (sd(inputData$message_gap, na.rm=T)-mean(inputData$message_gap, na.rm=T))/(sd(inputData$message_gap, na.rm=T)+mean(inputData$message_gap, na.rm=T))
chat_out = cbind(total_recorded_time, num_messages, message_numchars_sum, num_unique_messagers, message_gap_x, message_gap_sd, burstiness_raw)
if(sentiment==TRUE) chat_out = cbind(chat_out, sent.cols)
########################################
# Create an individual-level output
# Note, the presumption is that user_name is something unique -- hopefully it is!
########################################
dt = data.table(inputData)
if(sentiment == TRUE) {
agg.dt = dt[,list(message_numchars_sum = sum(message_numchars, na.rm=T), num_messages = .N, message_numchars_x = mean(message_numchars), message_numchars_sd = sd(message_numchars), message_gap_x = mean(message_gap, na.rm=T), message_gap_sd = sd(message_gap, na.rm=T),
message_positive_pct = sum(sent_class=="POSITIVE")/.N, message_positive_x = mean(Positive, na.rm=T), message_positive_sd = sd(Positive, na.rm=T),
message_negative_pct = sum(sent_class=="NEGATIVE")/.N, message_negative_x = mean(Negative, na.rm=T), message_negative_sd = sd(Negative, na.rm=T),
message_neutral_pct = sum(sent_class=="NEUTRAL")/.N, message_neautral_x = mean(Neutral, na.rm=T), message_neutral_sd = sd(Neutral, na.rm=T),
message_mixed_pct = sum(sent_class=="MIXED")/.N, message_mixed_x = mean(Mixed, na.rm=T), message_mixed_sd = sd(Mixed, na.rm=T)
), by=list(get(speakerId))]
names(agg.dt)[1] = speakerId
} else {
agg.dt = dt[,list(message_numchars_sum = sum(message_numchars, na.rm=T), num_messages = .N, message_numchars_x = mean(message_numchars), message_numchars_sd = sd(message_numchars), message_gap_x = mean(message_gap, na.rm=T), message_gap_sd = sd(message_gap, na.rm=T)
), by=list(get(speakerId))]
names(agg.dt)[1] = speakerId
}
agg.out = data.frame(agg.dt)
res.out = list("CHAT-LEVEL" = chat_out, "USER-LEVEL" = agg.out)
}
}