def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '-M', required=True,
help='Message file')
parser.add_argument('-p', '-P', action='store_true')
parser.add_argument('-s', '-S', required=True,
help='filename to store polarity in, no extension needed')
parser.add_argument('-f', '-F', required=True,
help='folder to store the files in, ending with /')
args = parser.parse_args()
messagefile = args.m
location_to_store = args.f
file_to_store = args.s
separate_sentiment = args.p
message_data = hlp.readcsv(messagefile)
message_header = message_data[0]
message_data = message_data[1:]
vader = vadersenti(data=message_data)
data = vader.compilesentiment(separate_sentiment_list=separate_sentiment)
if separate_sentiment:
hlp.dumpvariable(data, file_to_store+'.list', location_to_store)
else:
message_header.append('pos')
message_header.append('neg')
message_header.append('neu')
message_header.append('compound')
final_data = [message_header] + data
hlp.writecsv(final_data, location_to_store + file_to_store + '.csv', delimiter_sym=',')
def main():
parser = argparse.ArgumentParser('Script to perform sentiment analysis using VADER')
parser.add_argument('-m', '-M', type=str, required=True,
help='Location of the message file')
parser.add_argument('-mt', '-MT', type=str, required=True, nargs='+',
help='types of messages to filter')
parser.add_argument('-f', '-F', type=str, required=True,
help='filename where data is stored, no extension needed')
parser.add_argument('-s', '-S', type=str, required=True,
help='location of folder to store the file, ends with a /')
parser.add_argument('-p', '-P', action='store_true',
help='flag to store polarities separately')
parser.add_argument('-w', '-W', type=str, required=False,
help='conduct weekly analysis, path to the survey data for '
'creating week information')
parser.add_argument('-l', '-L', type=str, nargs='+', required=True,
help='the filters to use, make one or more choices: seenB, wasB, didB')
parser.add_argument('-lf', '-LF', type=str, nargs='+', required=True,
help='location of filtered data, from runSurveyStats.py, in same order as -l/L flag')
args = parser.parse_args()
message_file = args.m
message_types = args.mt
filename_to_store = args.f
location_to_store = args.s
separate_polarity_score = args.p
survey_file = args.w
filters_chosen = args.l
filter_files = args.lf
catch_all_data = hlp.getfilterdata(filters_chosen, filter_files, catch_all=True)
if separate_polarity_score and survey_file is not None:
print 'Cannot have separate polarity scores and weekly analysis together, ' \
'please remove the -p/-P flag'
return
if survey_file is not None:
wi = weeklyinfo()
week_dates = wi.getweeklyfo(survey_file)
gh = ghelper()
ff = filterfields(message_file)
data = []
for message_type in message_types:
data.extend(ff.filterbyequality(pr.m_type, message_type))
pid_dict = hlp.getuniqueparticipants(data, 'all' if len(message_types) > 1 else message_types[0])
sentiment_analyzer = vadersenti(data[1:])
returned_data = sentiment_analyzer.compilesentiment(pr.m_content, separate_sentiment_list=separate_polarity_score)
if separate_polarity_score:
hlp.dumpvariable(returned_data, filename_to_store + '.data', location_to_store)
else:
header = pr.message_header + ['pos', 'neg', 'neu', 'compound']
final_data = [header] + returned_data
hlp.writecsv(final_data, location_to_store + filename_to_store + '.csv')
weekly_data = gh.filterweeklydata(pid_dict, returned_data, week_dates,
'all' if len(message_types) > 1 else message_types[0])
hlp.dumpvariable(weekly_data, 'weekly_data.dict', location_to_store)
summarized_sentiment = {}
for pid in weekly_data:
summarized_sentiment[pid] = {}
participant_data = weekly_data[pid]
for week_no in participant_data:
summarized_sentiment[pid][week_no] = sentiment_analyzer.summarizesentiment(participant_data[week_no],
separate_in_out=True,
message_type=message_type)
hlp.dumpvariable(summarized_sentiment, 'weekly_summarized_sentiment.dict', location_to_store)
plt = plots()
overlay_data = gh.createbullyingoverlay(catch_all_data, week_dates, ff)
plt.plotweeklyprogression(summarized_sentiment, location_to_store, 'Sentiment Progress', 'Week',
'Sentiment Value', sentiment_legend=['Positive', 'Negative', 'Neutral'],
overlay_data=overlay_data)
print 'done'
