## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ---- error=FALSE, message=FALSE, warning=FALSE, include=FALSE, results='hide'---- library(zoomGroupStats) batchOut = invisible(batchProcessZoomOutput(batchInput=system.file('extdata', 'myMeetingsBatch.xlsx', package = 'zoomGroupStats'))) ## ---- eval=TRUE--------------------------------------------------------------- # Three records from the sample transcript dataset head(batchOut$transcript, 3) ## ---- eval=TRUE--------------------------------------------------------------- # Three records from the sample transcript dataset head(batchOut$chat, 3) ## ---- eval=FALSE-------------------------------------------------------------- # # You can request both sentiment analysis methods by including them in sentMethods # transcriptSent = textSentiment(inputData=batchOut$transcript, idVars=c('batchMeetingId','utteranceId'), textVar='utteranceMessage', sentMethods=c('aws', 'syuzhet'), appendOut=FALSE, languageCodeVar='utteranceLanguage') # # # This does only the aws sentiment analysis on a chat file # chatSent = textSentiment(inputData=batchOut$chat, idVars=c('batchMeetingId', 'messageId'), textVar='message', sentMethods=c('aws'), appendOut=FALSE, languageCodeVar='messageLanguage') ## ---- eval=TRUE--------------------------------------------------------------- # This does only the syuzhet analysis on the transcript and appends does not append it to the input dataset transcriptSent = textSentiment(inputData=batchOut$transcript, idVars=c('batchMeetingId','utteranceId'), textVar='utteranceMessage', sentMethods=c('syuzhet'), appendOut=FALSE, languageCodeVar='utteranceLanguage') head(transcriptSent$syuzhet) ## ---- eval=TRUE--------------------------------------------------------------- # This does only the syuzhet sentiment analysis on a chat file and appends it to the input dataset chatSent = textSentiment(inputData=batchOut$chat, idVars=c('batchMeetingId', 'messageId'), textVar='message', sentMethods=c('syuzhet'), appendOut=TRUE, languageCodeVar='messageLanguage') head(chatSent$syuzhet) ## ---- eval=TRUE--------------------------------------------------------------- # Analyze the transcript, without the sentiment metrics convoTrans = textConversationAnalysis(inputData=batchOut$transcript, inputType='transcript', meetingId='batchMeetingId', speakerId='userName') ## ---- eval=TRUE--------------------------------------------------------------- # This is output at the meeting level. (Note that the values across meetings are equivalent because the sample dataset is a replication of the same meeting multiple times.) head(convoTrans$transcriptlevel) ## ---- eval=TRUE--------------------------------------------------------------- # This is output at the speaker level head(convoTrans$speakerlevel) ## ---- eval=TRUE--------------------------------------------------------------- # Analyze the conversation within the chat file, including the sentiment metrics convoChat = textConversationAnalysis(inputData=chatSent$syuzhet, inputType='chat', meetingId='batchMeetingId', speakerId='userName', sentMethod="syuzhet") ## ---- eval=TRUE--------------------------------------------------------------- # This is output at the meeting level head(convoChat$chatlevel) ## ---- eval=TRUE--------------------------------------------------------------- # This is output at the speaker level head(convoChat$userlevel) ## ---- eval=TRUE--------------------------------------------------------------- win.convo.out = windowedTextConversationAnalysis(inputData=batchOut$transcript, inputType='transcript', meetingId='batchMeetingId', speakerId='userName', sentMethod="none", timeVar="utteranceStartSeconds", windowSize=300) ## ---- eval=TRUE--------------------------------------------------------------- # View the window-level output head(win.convo.out$windowlevel) ## ---- eval=TRUE--------------------------------------------------------------- # View the output for speakers within windows head(win.convo.out$speakerlevel)