def main():
parser = argparse.ArgumentParser()
parser.add_argument('-d', '-D', required=True,
help='labelled csv')
parser.add_argument('-f', '-F', required=True,
help='folder to save the data in')
args = parser.parse_args()
data_file = args.d
location_to_store = args.f
all_afinn_data = hlp.readcsv(data_file)
labelled_data = hlp.processafinnsentiment(all_afinn_data)
csv_header = ['pid', 'm_type',
'in_pos', 'in_neg', 'in_neu',
'out_pos', 'out_neg', 'out_neu',
'in_deg_part', 'in_deg_nonpart',
'out_deg_part', 'out_deg_nonpart']
pol_dist, complete_in_out = distribution_polarity(labelled_data)
print '***For Complete Dataset***'
print 'Incoming(P, N, U): ', complete_in_out['in']
print 'Outgoing(P, N, U): ', complete_in_out['out']
hlp.dumpvariable([pol_dist, complete_in_out], 'polarity_in_out.dict', location_to_store)
to_store_csv = [csv_header]
for pid in pol_dist:
pid_data = pol_dist[pid]
for m_type in pid_data:
m_data = pid_data[m_type]
csv_line = __summarize_data(m_data)
final_csv_line = [pid, m_type]
final_csv_line.extend(csv_line)
to_store_csv.append(final_csv_line)
hlp.writecsv(to_store_csv, location_to_store+'polarity_in_out.csv')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '-M', required=True,
help='Sentiment Message file')
parser.add_argument('-t', '-T', action='store_true',
help='Sentiment type flag, if used then vader, else afinn')
parser.add_argument('-f', '-F', required=True,
help='Folder to store checkpoints, and final result')
parser.add_argument('-w', '-W', required=False,
help='Per week/month analysis')
args = parser.parse_args()
message_file = args.m
sentiment_type = args.t
location_to_store = args.f
survey_file = args.w
# get message data, only sms and fb_message
ff = filterfields(message_file)
ff.setdata(ff.getdata()[1:])
sms_data = ff.filterbyequality(pr.m_type, 'sms')
pid_dict_sms = hlp.getuniqueparticipants2(sms_data)
fb_message_data = ff.filterbyequality(pr.m_type, 'fb_message')
pid_dict_fb = hlp.getuniqueparticipants2(fb_message_data)
message_data = sms_data + fb_message_data
# put the labels on
labelled_data = hlp.processvadersentiment(message_data, label_only=False) if sentiment_type else \
hlp.processafinnsentiment(message_data, label_only=False)
if survey_file is not None:
wi = weeklyinfo()
weekly_info = wi.getweeklyfo(survey_file)
weekly_data = hlp.divideintoweekly(labelled_data, weekly_info, ff)
#__temp_testing_for_discrepancy(labelled_data, weekly_data)
# get the pid_dict for easier handling
pid_dict = hlp.getuniqueparticipants2(labelled_data)
if survey_file is not None:
over_sent, in_sent, out_sent, xtick, ytick = per_participant_sentiment(weekly_data, pid_dict['participants'])
__plot_imshow(over_sent, 'Participant', 'Week #', xtick, ytick, location_to_store+'sent_imshow_over.pdf')
__plot_imshow(in_sent, 'Participant', 'Week #', xtick, ytick, location_to_store+'sent_imshow_in.pdf')
__plot_imshow(out_sent, 'Participant', 'Week #', xtick, ytick, location_to_store+'sent_imshow_out.pdf')
print '***SMS***'
print 'P: ', len(pid_dict_sms['participants'].values()), ' NP: ', len(pid_dict_sms['nonparticipants'].values())
print '***FB***'
print 'P: ', len(pid_dict_fb['participants'].values()), 'NP: ', len(pid_dict_fb['nonparticipants'].values())
print '***OVERALL***'
print 'P: ', len(pid_dict['participants'].values()), 'NP: ', len(pid_dict['nonparticipants'].values())
summary_src_trg = summarize_message_by_src_trg(labelled_data)
print '***Message Distribution***'
for m_type_1 in summary_src_trg:
print m_type_1, summary_src_trg[m_type_1]
if survey_file is not None:
week_list = weekly_data.keys()
week_list.sort()
# this is not good, as there aren't enough triads
months = [[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
[17, 18, 19, 20],
[21, 22, 23, 24, 25]]
# this has at least 8 triads, always, use this
months2 = [[1, 2, 3, 4, 5, 6, 7, 8],
[9, 10, 11, 12, 13, 14, 15, 16],
[17, 18, 19, 20, 21, 22, 23, 24, 25]]
month_idx = 1
for month in months2:
labelled_data = []
for week in month:
labelled_data.extend(weekly_data[week])
general_graph, random_graph = conduct_triad_analysis(labelled_data, pid_dict)
frac_triad = general_graph[3]
summary_triad = general_graph[2]
frac_triad_rand = random_graph[3]
summary_triad_rand = random_graph[2]
print '** Months ', 2*month_idx-1, 2*month_idx, ': ', month,' ***'
print 'len(LD): ', len(labelled_data)
for summary in frac_triad:
print summary, 'Study: ', frac_triad[summary], '(', len(summary_triad[summary]), ')', ' Random: ', \
frac_triad_rand[summary], '(', len(summary_triad_rand[summary]), ')'
words_list, short_list = word_count(labelled_data)
toWrite_wl_csv = create_word_count_csv(words_list)
hlp.writecsv(toWrite_wl_csv, location_to_store+'word_list_'+str(2*month_idx-1)+'-'+str(2*month_idx)+'.csv',
delimiter_sym=',')
for mtype in words_list:
counted_words = Counter(words_list[mtype])
counted_short = Counter(short_list[mtype])
print '***For '+mtype+' ***'
print 'Top 20 words: ', __get_top_word_sentiment(counted_words.most_common(20))
print 'Top 20 short: ', counted_short.most_common(20)
print '\n\n'
hlp.dumpvariable([general_graph, random_graph, labelled_data, pid_dict], 'month_'+str(month_idx)+'.list', location_to_store)
month_idx += 1
else:
print 'len(LD): ', len(labelled_data)
words_list, short_list = word_count(labelled_data)
toWrite_wl_csv = create_word_count_csv(words_list)
hlp.writecsv(toWrite_wl_csv, location_to_store+'word_list.csv', delimiter_sym=',')
for mtype in words_list:
counted_words = Counter(words_list[mtype])
counted_short = Counter(short_list[mtype])
print '***For '+mtype+' ***'
print 'Top 20 words: ', __get_top_word_sentiment(counted_words.most_common(20))
print 'Top 20 short: ', counted_short.most_common(20)
print '\n\n'
general_graph, random_graph = conduct_triad_analysis(labelled_data, pid_dict)
frac_triad = general_graph[3]
summary_triad = general_graph[2]
frac_triad_rand = random_graph[3]
summary_triad_rand = random_graph[2]
for summary in frac_triad:
print summary, 'Study: ', frac_triad[summary], '(', len(summary_triad[summary]), ')', ' Random: ', \
frac_triad_rand[summary], '(', len(summary_triad_rand[summary]), ')'
hlp.dumpvariable([general_graph, random_graph, labelled_data, pid_dict], 'Overall.list', location_to_store)
# plot_degree_dist(general_graph[4], 'Degree(d)', '# of Participants with Degree d')
pos, neg, neu = get_polarity_directionality(labelled_data)
print '***Polarity Distribution***'
print 'Positive: \n', pos
print 'Negative: \n', neg
print 'Neutral: \n', neu
in_m, out_m, in_d, out_d = get_count_degrees_messages_directed(labelled_data, pid_dict['participants'])
print '***Incoming Messages***'
print 'Total: ', sum(in_m), 'Mean: ', np.mean(in_m), 'Std. dev.: ', np.std(in_m)
print '***Outgoing Messages***'
print 'Total: ', sum(out_m), 'Mean: ', np.mean(out_m), 'Std. dev.: ', np.std(out_m)
print '***In Degree***'
print 'Total: ', sum(in_d), 'Mean: ', np.mean(in_d), 'Std. dev.: ', np.std(in_d)
print '***Out Degree***'
print 'Total: ', sum(out_d), 'Mean: ', np.mean(out_d), 'Std. dev.: ', np.std(out_d)
print '***COUNTS***'
plot_messages_degree([in_m, out_m], '# of Messages', 'Cumulative Participant Prob.',
location_to_store+'in_out_messages.pdf')
# plot_messages_degree(out_m, '# of Outgoing Messages', 'Cumulative Participant Prob.',
# location_to_store+'out_messages.pdf')
plot_messages_degree([in_d, out_d], 'Degree', 'Cumulative Participant Prob.',
location_to_store+'in_out_degree.pdf', True)
# plot_messages_degree(out_d, 'Out Degree', 'Cumulative Participant Prob.',
# location_to_store+'out_degree.pdf', True)
print 'TADAA!!'
