Helper function to aggregate sentiment variables

Description

Used to aggregate the sentiment variables to the individual and meeting levels

Usage

aggSentiment(inputData, meetingId = NULL, speakerId = NULL, sentMethod)

Arguments

Value

A data.frame giving the sentiment metrics aggregated to the requested level. If only meetingId is specified, metrics are aggregated to that level. If only speakerId is specified, metrics are aggregated to the individual level across any meetings. If both meetingId and speakerId are specified, metrics are aggregated to the level of the individual within meeting.

Examples

agg.out = aggSentiment(inputData=sample_transcript_sentiment_aws, 
meetingId="batchMeetingId", speakerId = "userId", sentMethod="aws")

agg.out = aggSentiment(inputData=sample_chat_sentiment_syu, 
meetingId="batchMeetingId", speakerId = "userName", sentMethod="syuzhet")

Batch process video files, breaking them into stills

Description

#' This helper calls grabVideoStills, which function currently relies on the av package and 'ffmpeg' to split a video file into images. This function will save the images to the director specified by the user.

Usage

batchGrabVideoStills(
  batchInfo,
  imageDir = NULL,
  overWriteDir = FALSE,
  sampleWindow
)

Arguments

Value

a data.frame that gives information about the batch. Each record corresponds to one video, with:

• batchMeetingId - the meeting identifier

• videoExists - boolean indicating whether the video file was there

• imageDir - path to the directory where video images are saved

• sampleWindow - integer with the sampleWindow requested

• numFramesExtracted - the number of image files that were saved

Examples

vidBatchInfo = batchGrabVideoStills(batchInfo=sample_batch_info,
imageDir=tempdir(), overWriteDir=TRUE, sampleWindow=2)
## Not run: 
vidBatchInfo = batchGrabVideoStills(batchInfo=zoomOut$batchInfo,
imageDir="~/Documents/myMeetings/videoImages", overWriteDir=TRUE,  sampleWindow=600)

## End(Not run)

Batch process files that have been downloaded from Zoom

Description

Provide the location of a structured batchInput file and this function will process a set of meetings at once.

Usage

batchProcessZoomOutput(batchInput, exportZoomRosetta = NULL)

Arguments

Value

a list that has a data.frame for each of the elements of a Zoom output that are available in the input directory:

• batchInfo - Each row is a meeting included in batchInput. Columns provide information about each meeting.

• meetInfo - Each row is a meeting for which there was a downloaded participants file. Columns provide information about the meeting from the Zoom Cloud recording site.

• partInfo - Each row is a Zoom display name (with display name changes in parentheses). Columns provide information about participants from the Zoom Cloud recording site.

• transcript - Each row is an utterance in the audio transcript. This is the output from processZoomTranscript.

• chat - Each row is a message posted to the chat. This is the output from processZoomChat.

• rosetta - Each row is a unique display name (within meeting) encountered in the batchInput. This is used to reconcile user identities.

Examples

batchOut = batchProcessZoomOutput(batchInput=system.file('extdata', 
'myMeetingsBatch.xlsx', package = 'zoomGroupStats'), 
exportZoomRosetta=file.path(tempdir(),"_rosetta.xlsx"))

Batch analyze faces in videos

Description

Using this function you can analyze attributes of facial expressions within a batch of video files. This batch approach requires breaking the videos into still frames in advance by using the batchGrabVideoStills() function.

Usage

batchVideoFaceAnalysis(
  batchInfo,
  imageDir,
  sampleWindow,
  facesCollectionID = NA
)

Arguments

Value

data.frame with one record for every face detected in each frame across all meetings. For each face, there is an abundance of information from 'AWS Rekognition'. This output is quite detailed. Note that there will be a varying number of faces per sampled frame in the video. Imagine that you have sampled the meeting and had someone rate each person's face within that sampled moment.

Examples

## Not run: 
  vidOut = batchVideoFaceAnalysis(batchInfo=zoomOut$batchInfo, 
  imageDir="~/Documents/meetingImages",
  sampleWindow = 300)

## End(Not run)

Create a file to aid in adding a unique identifier to link to the zoom user name

Description

A major challenge in analyzing virtual meetings is reconciling the display name that zoom users in chat and transcript. This function outputs a data.frame that can be helpful in manually adding a new unique identifier to use in further data anlaysis.

Usage

createZoomRosetta(zoomOutput)

Arguments

Value

a data.frame that has unique values for the zoom display name that show up across any files that are available, including participants, transcript, and chat. If the user gives the participants file, it will separate display name changes and include all versions. If there are emails attached to display names, it will include those.

Examples

rosetta.out = createZoomRosetta(processZoomOutput(fileRoot=
file.path(system.file('extdata', package = 'zoomGroupStats'),"meeting001")))
## Not run: 
rosetta.out = createZoomRosetta(processZoomOutput(fileRoot="~/zoomMeetings/meeting001"))

## End(Not run)

Helper function to split a video into still frames

Description

This function currently relies on the av package and 'ffmpeg' to split a video file into images. This function will save the images to the directory specified by the user.

Usage

grabVideoStills(
  inputVideo,
  imageDir = NULL,
  overWriteDir = FALSE,
  sampleWindow
)

Arguments

Value

a data.frame that gives information about the still frames. Each record is a stillframe, with the following info:

• imageSeconds - number of seconds from the start of the video when this image was captured

• imageName - full path to where the image has been saved as a .png

Examples

vidOut = grabVideoStills(inputVideo=system.file('extdata', "meeting001_video.mp4", 
package = 'zoomGroupStats'), imageDir=tempdir(), overWriteDir=TRUE, sampleWindow=2)
## Not run: 
grabVideoStills(inputVideo='myMeeting.mp4', 
imageDir="~/Documents/myMeetings/videoImages", overWriteDir=TRUE,  sampleWindow=45)

## End(Not run)

Helper function to add unique identifiers to processed Zoom downloads

Description

Import an edited zoomRosetta file that tells how to link up Zoom display names to some unique individual identifier

Usage

importZoomRosetta(zoomOutput, zoomRosetta, meetingId)

Arguments

Value

returns zoomOutput with identifiers in zoomRosetta merged to any available data.frames in the zoomOutput file

Examples

batchOutIds = importZoomRosetta(zoomOutput=
batchProcessZoomOutput(batchInput=system.file('extdata', 
'myMeetingsBatch.xlsx', package = 'zoomGroupStats')), 
zoomRosetta=system.file('extdata', 
'myMeetingsBatch_rosetta_edited.xlsx', package = 'zoomGroupStats'), 
meetingId="batchMeetingId")

## Not run: 
batchOutIds = importZoomRosetta(zoomOutput=batchOut, zoomRosetta="myEditedRosetta.xlsx", 
meetingId="batchMeetingId")

## End(Not run)

Helper function that creates temporal windows in datasets

Description

This creates a set of temporal windows of specified size so that metrics can be computed within those windows.

Usage

makeTimeWindows(inputData, timeVar, windowSize)

Arguments

Value

list with two data.frames:

• windowedData - inputData with the temporal window identifying information included

• allWindows - contains the full set of temporal windows and identifying information. This is valuable because inputData may not have records within all of the possible temporal windows

Examples

win.out = makeTimeWindows(sample_transcript_processed, 
timeVar="utteranceStartSeconds", windowSize=10)

Process a Zoom chat file

Description

Parses the data from the chatfile that is downloaded from the Zoom Cloud recording site. Note that this is the file that accompanies a recording. This is not the file that you might download directly within a given Zoom session, nor is it 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.

Usage

processZoomChat(
  fname,
  sessionStartDateTime = "1970-01-01 00:00:00",
  languageCode = "en"
)

Arguments

Value

data.frame where each record is a message submission in the chat, containing columns:

• messageId - Numeric identifier for each message, only unique within a given meeting

• messageSeconds - When message was posted, in number of seconds from start of session

• messageTime - When message was posted as POSIXct, using the supplied sessionStartDateTime

• userName - Display name of user who posted the message

• message - Text of the message that was posted

• messageLanguage - Language code for the message

Examples

ch.out = processZoomChat(
fname=system.file('extdata', "meeting001_chat.txt", package = 'zoomGroupStats'), 
sessionStartDateTime = '2020-04-20 13:30:00', 
languageCode = 'en')

Wrapper function to process the raw files from Zoom in a single call

Description

The user provides a fileRoot that is used for a given meeting. Output files should be named as fileRoot_chat.txt; fileRoot_transcript.vtt; and fileRoot_participants.csv. Any relevant files will be processed.

Usage

processZoomOutput(
  fileRoot,
  rosetta = TRUE,
  sessionStartDateTime = "1970-01-01 00:00:00",
  recordingStartDateTime = "1970-01-01 00:00:00",
  languageCode = "en"
)

Arguments

Value

a named list containing data.frames for each of the available files:

• meetInfo - A single row with info for the meeting that is in the participants file. Columns provide information about the meeting from the Zoom Cloud recording site.

• partInfo - Each row is a Zoom display name (with display name changes in parentheses). Columns provide information about participants from the Zoom Cloud recording site.

• transcript - Each row is an utterance in the audio transcript. This is the output from processZoomTranscript.

• chat - Each row is a message posted to the chat. This is the output from processZoomChat.

• rosetta - Each row is a unique display name (within meeting) encountered in the batchInput. This is used to reconcile user identities.

Examples

zoomOut = processZoomOutput(fileRoot=file.path(
system.file('extdata', package = 'zoomGroupStats'),"meeting001"
), rosetta=TRUE)
## Not run: 
zoomOut = processZoomOutput(fileRoot="~/zoomMeetings/myMeeting", rosetta=TRUE)

## End(Not run)

Process participant information from a Zoom meeting export

Description

This function parses the information from the downloadable meeting information file in Zooms reports section. The function presumes that you have checked the box to include the meeting information in the file. That means that there is a header (2 rows) containing the zoom meeting information. Following that header are four columns: Name of user, user email, total duration, and guest.

Usage

processZoomParticipantsInfo(inputPath)

Arguments

Value

list of two data.frames with parsed information from the downloadable Zoom participants file

• meetInfo - provides the meeting level information that Zoom Cloud gives

• partInfo - provides the participant level information that Zoom Cloud gives

Examples

partInfo = processZoomParticipantsInfo(
system.file('extdata', "meeting001_participants.csv", package = 'zoomGroupStats')
)

Process Zoom transcript file

Description

Process Zoom transcript file

Usage

processZoomTranscript(
  fname,
  recordingStartDateTime = "1970-01-01 00:00:00",
  languageCode = "en"
)

Arguments

Value

data.frame where each record is an utterance in the transcript, with columns:

• utteranceId - Numeric identifier for each utterance in the transcript

• utteranceStartSeconds - number of seconds from the start of the recording when utterance began

• utteranceStartTime - POSIXct timestamp of the start of the utterance, using recordingStartDateTime as the zero

• utteranceEndSeconds - number of seconds from the start of the recording when utterance ended

• utteranceEndTime - POSIXct timestamp of the end of the utterance, using recordingStartDateTime as the zero

• utteranceTimeWindow - number of seconds that this utterance lasted

• userName - Zoom display name of the person who spoke this utterance

• utteranceMessage - transcribed spoken words of this utterance

• utteranceLanguage - language code for this utterance

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.

Examples

tr.out = processZoomTranscript(
fname=system.file('extdata', 'meeting001_transcript.vtt', package = 'zoomGroupStats'), 
recordingStartDateTime = '2020-04-20 13:30:00', languageCode = 'en')

Parsed batch info file in a recorded 'Zoom' meeting

Description

Parsed batch info file in a recorded 'Zoom' meeting

Usage

sample_batch_info

Format

A data frame with 3 rows of 13 variables:

batchMeetingId

a character meeting identification variable

fileRoot

the prefix to the files for this particular meeting

participants

binary indicating whether there is a participants file downloaded

transcript

binary indicating whether there is a transcript file downloaded

chat

binary indicating whether there is a chat file downloaded

video

binary indicating whether there is a video file downloaded

sessionStartDateTime

start of the actual session as a character YYYY-MM-DD HH:MM:SS

recordingStartDateTime

start of the actual recording as a character YYYY-MM-DD HH:MM:SS

participants_processed

binary indicating whether there is a participants file already processed

transcript_processed

binary indicating whether there is a transcript file already processed

chat_processed

binary indicating whether there is a chat file already processed

video_processed

binary indicating whether there is a video file already processed

dirRoot

character giving the directory in which all files will be found

Source

http://zoomgroupstats.org/

Parsed chat file in a 'Zoom' meeting

Description

Parsed chat file in a 'Zoom' meeting

Usage

sample_chat_processed

Format

A data frame with 30 rows of 9 variables:

batchMeetingId

a character meeting identification variable

userName

'Zoom' display name attached to this speaker

messageId

an incremented numeric identifier for a marked chat message

messageSeconds

when the message was posted as the number of seconds from the start of the recording

messageTime

timestamp for message

message

text of the message

messageLanguage

language code of the message

userEmail

character email address

userId

numeric id of each speaker

Source

http://zoomgroupstats.org/

Parsed chat file in a 'Zoom' meeting with sentiment analysis using AWS

Description

Parsed chat file in a 'Zoom' meeting with sentiment analysis using AWS

Usage

sample_chat_sentiment_aws

Format

A data frame with 10 rows of 14 variables:

batchMeetingId

a character meeting identification variable

messageId

an incremented numeric identifier for a marked chat message

userName

'Zoom' display name attached to the messager

messageSeconds

when the message was posted as the number of seconds from the start of the recording

messageTime

timestamp for message

message

text of the message

messageLanguage

language code of the message

userEmail

character email address

userId

numeric id of each speaker

aws_sentClass

character giving the sentiment classification of this text

aws_positive

probability that this text is mixed emotion

aws_negative

probability that this text is negative

aws_neutral

probability that this text is neutral

aws_mixed

probability that this text is positive

Source

http://zoomgroupstats.org/

Parsed chat file in a 'Zoom' meeting with sentiment analysis using syuzhet

Description

Parsed chat file in a 'Zoom' meeting with sentiment analysis using syuzhet

Usage

sample_chat_sentiment_syu

Format

A data frame with 30 rows of 30 variables:

batchMeetingId

a character meeting identification variable

messageId

an incremented numeric identifier for a marked chat message

userName

'Zoom' display name attached to the messager

messageSeconds

when the message was posted as the number of seconds from the start of the recording

messageTime

timestamp for message

message

text of the message

messageLanguage

language code of the message

userEmail

character email address

userId

numeric id of each speaker

wordCount

number of words in this utterance

syu_anger

number of anger words

syu_anticipation

number of anticipation words

syu_disgust

number of disgust words

syu_fear

number of fear words

syu_joy

number of joy words

syu_sadness

number of sadness words

syu_surprise

number of surprise words

syu_trust

number of trust words

syu_negative

number of negative words

syu_positive

number of positive words

Source

http://zoomgroupstats.org/

Parsed spoken language in a 'Zoom' meeting.

Description

Parsed spoken language in a 'Zoom' meeting.

Usage

sample_transcript_processed

Format

A data frame with 30 rows of 12 variables:

batchMeetingId

a character meeting identification variable

userName

'Zoom' display name attached to this speaker

utteranceId

an incremented numeric identifier for a marked speech utterance

utteranceStartSeconds

when the utterance started as the number of seconds from the start of the recording

utteranceStartTime

timestamp for the start of the utterance

utteranceEndSeconds

when the utterance ended as the number of seconds from the start of the recording

utteranceEndTime

timestamp for the end of the utterance

utteranceTimeWindow

duration of the utterance, in seconds

utteranceMessage

the text of the utterance

utteranceLanguage

language code of the utterance

userEmail

character email address

userId

numeric id of each speaker

Source

http://zoomgroupstats.org/

Parsed spoken language in a 'Zoom' meeting with AWS-based sentiment analysis.

Description

Parsed spoken language in a 'Zoom' meeting with AWS-based sentiment analysis.

Usage

sample_transcript_sentiment_aws

Format

A data frame with 30 rows of 17 variables:

batchMeetingId

a character meeting identification variable

utteranceId

an incremented numeric identifier for a marked speech utterance

userName

'Zoom' display name attached to this speaker

utteranceStartSeconds

when the utterance started as the number of seconds from the start of the recording

utteranceStartTime

timestamp for the start of the utterance

utteranceEndSeconds

when the utterance ended as the number of seconds from the start of the recording

utteranceEndTime

timestamp for the end of the utterance

utteranceTimeWindow

duration of the utterance, in seconds

utteranceMessage

the text of the utterance

utteranceLanguage

language code of the utterance

userEmail

character email address

userId

numeric id of each speaker

aws_sentClass

character giving the sentiment classification of this text

aws_positive

probability that this text is mixed emotion

aws_negative

probability that this text is negative

aws_neutral

probability that this text is neutral

aws_mixed

probability that this text is positive

Source

http://zoomgroupstats.org/

Parsed spoken language in a 'Zoom' meeting with syuzhet-based sentiment analysis.

Description

Parsed spoken language in a 'Zoom' meeting with syuzhet-based sentiment analysis.

Usage

sample_transcript_sentiment_syu

Format

A data frame with 30 rows of 23 variables:

batchMeetingId

a character meeting identification variable

utteranceId

an incremented numeric identifier for a marked speech utterance

userName

'Zoom' display name attached to this speaker

utteranceStartSeconds

when the utterance started as the number of seconds from the start of the recording

utteranceStartTime

timestamp for the start of the utterance

utteranceEndSeconds

when the utterance ended as the number of seconds from the start of the recording

utteranceEndTime

timestamp for the end of the utterance

utteranceTimeWindow

duration of the utterance, in seconds

utteranceMessage

the text of the utterance

utteranceLanguage

language code of the utterance

userEmail

character email address

userId

numeric id of each speaker

wordCount

number of words in this utterance

syu_anger

number of anger words

syu_anticipation

number of anticipation words

syu_disgust

number of disgust words

syu_fear

number of fear words

syu_joy

number of joy words

syu_sadness

number of sadness words

syu_surprise

number of surprise words

syu_trust

number of trust words

syu_negative

number of negative words

syu_positive

number of positive words

Source

http://zoomgroupstats.org/

Analyze conversation attributes

Description

This function takes in the output of one of the other functions (either processZoomChat or processZoomTranscript) and produces a set of conversation measures.

Usage

textConversationAnalysis(
  inputData,
  inputType,
  meetingId,
  speakerId,
  sentMethod = "none"
)

Arguments

Value

A list of two data.frames, with names conditional on your choice to analyze a parsed transcript file or a parsed chat file. The first list item contains statistics at the corpus level. The second list item contains statistics at the speaker/messager level of analysis.

Examples

convo.out = textConversationAnalysis(inputData=sample_transcript_processed, 
inputType='transcript', meetingId='batchMeetingId', 
speakerId='userName', sentMethod="none")

convo.out = textConversationAnalysis(inputData=sample_transcript_sentiment_syu, 
inputType='transcript', meetingId='batchMeetingId', 
speakerId='userName', sentMethod="syuzhet")

convo.out = textConversationAnalysis(inputData=sample_chat_sentiment_aws, 
inputType='chat', meetingId='batchMeetingId', 
speakerId='userName', sentMethod="aws")

## Not run: 
convo.out = textConversationAnalysis(inputData=sample_transcript_sentiment_aws, 
inputType='transcript', meetingId='batchMeetingId', 
speakerId='userName', sentMethod="aws")

convo.out = textConversationAnalysis(inputData=sample_transcript_sentiment_syu, 
inputType='transcript', meetingId='batchMeetingId', 
speakerId='userName', sentMethod="syuzhet")

convo.out = textConversationAnalysis(inputData=sample_chat_processed, 
inputType='chat', meetingId='batchMeetingId', 
speakerId='userName', sentMethod="none")

convo.out = textConversationAnalysis(inputData=sample_chat_sentiment_aws, 
inputType='chat', meetingId='batchMeetingId', 
speakerId='userName', sentMethod="aws")

convo.out = textConversationAnalysis(inputData=sample_chat_sentiment_syu, 
inputType='chat',meetingId='batchMeetingId',  
speakerId='userName', sentMethod="syuzhet")

## End(Not run)

Conduct a sentiment analysis on text data

Description

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 the aws option, you must have an aws account that with privileges for the comprehend service However you authenticate for AWS, you should do so before running calling the function with this option in sentMethods

Usage

textSentiment(
  inputData,
  idVars,
  textVar,
  sentMethods,
  appendOut = FALSE,
  languageCodeVar
)

Arguments

Value

returns a list containing as data.frames the output of the sentiment analyses that were requested in sentMethods. For each output data.frame, the first columns are the idVars specified to enable combining back with the original inputData

Examples

sent.out = textSentiment(inputData=sample_chat_processed,
idVars=c('batchMeetingId', 'messageId'), 
textVar='message', sentMethods='syuzhet',appendOut=TRUE,
languageCodeVar='messageLanguage')

## Not run: 
sent.out = textSentiment(inputData=sample_transcript_processed, 
idVars=c('batchMeetingId','utteranceId'), 
textVar='utteranceMessage', sentMethods=c('aws','syuzhet'), 
appendOut=TRUE, languageCodeVar='utteranceLanguage')

## End(Not run)

Simple conversational turn-taking analysis

Description

Generate a very basic analysis of the conversational turntaking in either a Zoom transcript or a Zoom chat file.

Usage

turnTaking(inputData, inputType, meetingId, speakerId)

Arguments

Value

list of four data.frames giving different levels of analysis for turn taking:

• rawTurn - This data.frame gives a dataset with a lagged column so that you could calculate custom metrics

• aggTurnsDyad - This gives a dyad-level dataset so that you know whose speech patterns came before whose

• aggTurnsSpeaker - This gives a speaker-level dataset with metrics that you could use to assess each given person's influence on the conversation

• aggTurnsSpeaker_noself - This is a replication of the aggTurnsSpeaker dataset, but it excludes turns where a speaker self-follows (i.e., Speaker A => Speaker A)

Examples

turn.out = turnTaking(inputData=sample_transcript_processed, 
inputType='transcript', meetingId='batchMeetingId', 
speakerId='userName')

turn.out = turnTaking(inputData=sample_chat_processed, 
inputType='chat', meetingId='batchMeetingId', 
speakerId='userName')

Analyze the facial features within an exported Zoom video file

Description

Using this function you can analyze attributes of facial expressions within a video file. There are two ways to supply the video information. First, you can provide the actual video file. The function will then break it down into still frames using the grabVideoStills() function. Second, you can use the videoImageDirectory argument to give the location of a directory where images have been pre-saved.

Usage

videoFaceAnalysis(
  inputVideo,
  recordingStartDateTime,
  sampleWindow,
  facesCollectionID = NA,
  videoImageDirectory = NULL,
  grabVideoStills = FALSE,
  overWriteDir = FALSE
)

Arguments

Value

data.frame with one record for every face detected in each frame. For each face, there is an abundance of information from 'AWS Rekognition'. This output is quite detailed. Note that there will be a varying number of faces per sampled frame in the video. Imagine that you have sampled the meeting and had someone rate each person's face within that sampled moment.

Examples

## Not run: 
vid.out = videoFaceAnalysis(inputVideo="meeting001_video.mp4", 
recordingStartDateTime="2020-04-20 13:30:00", 
sampleWindow=1, facesCollectionID="group-r",
videoImageDirectory="~/Documents/meetingImages", 
grabVideoStills=FALSE, overWriteDir=FALSE)

## End(Not run)

Run a windowed analysis on either a Zoom transcript or chat This function conducts a temporal window analysis on the conversation in either a Zoom transcript or chat. It replicates the textConversationAnalysis function across a set of windows at a window size specified by the user.

Description

Run a windowed analysis on either a Zoom transcript or chat This function conducts a temporal window analysis on the conversation in either a Zoom transcript or chat. It replicates the textConversationAnalysis function across a set of windows at a window size specified by the user.

Usage

windowedTextConversationAnalysis(
  inputData,
  inputType,
  meetingId,
  speakerId,
  sentMethod = "none",
  timeVar = "automatic",
  windowSize
)

Arguments

Value

list with two data.frames. In the first (windowlevel), each row is a temporal window. In the second (speakerlevel), each row is a user's metrics within a given temporal window.

Examples

win.text.out = windowedTextConversationAnalysis(inputData=sample_transcript_sentiment_aws, 
inputType="transcript", meetingId="batchMeetingId", speakerId="userName", sentMethod="aws", 
timeVar="utteranceStartSeconds", windowSize=600)