def stylistic_features():
data = []
data_target = []
for pair, conversation in speaker_pairs.iteritems():
this_vector = []
replies_x = get_replies(conversation, "x")
replies_y = get_replies(conversation, "y")
# print pair
# # print conversation
# # print ""
# # print ""
# print "REPLIES_X " + replies_x
# print "REPLIES_Y " + replies_y
# print ""
# print ""
avg_x = len(utils.tokenize_utterance(replies_x)) / len(
conversation
) ##using future import above to have float number out of int division
avg_y = len(utils.tokenize_utterance(replies_y)) / len(conversation)
x_marker_count = utils.get_liwc_counts_from_utterance(replies_x)
y_marker_count = utils.get_liwc_counts_from_utterance(replies_y)
this_vector = this_vector + list(x_marker_count) + list(y_marker_count)
this_vector.append(avg_x)
this_vector.append(avg_y)
x = all_utterances[conversation[0][0]]
y = all_utterances[conversation[0][1]]
if x["is_justice"] and not y["is_justice"]:
label = high
elif not x["is_justice"] and y["is_justice"]:
label = low
else:
label = error
if label != error:
data.append(this_vector)
data_target.append(label)
# print "Data" + str(data)
# print "Target" + str(data_target)
return (data, data_target)
def count_exhibits_feature(utterance_pair, speaker):
utterance = all_utterances[utterance_pair[speaker]]["utterance"]
utter_vec = utils.get_liwc_counts_from_utterance(utterance)
exhibits_feature_counts = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
for marker_id in range(0, 8):
if utter_vec[marker_id] > 0:
exhibits_feature_counts[marker_id] = exhibits_feature_counts[marker_id] + 1.0
return exhibits_feature_counts
def count_coordination(utterance_pair):
b_utterance = all_utterances[utterance_pair[1]]["utterance"]
a_utterance = all_utterances[utterance_pair[0]]["utterance"]
b_utter_vec = utils.get_liwc_counts_from_utterance(b_utterance)
a_utter_vec = utils.get_liwc_counts_from_utterance(a_utterance)
tokenized_b = utils.tokenize_utterance(b_utterance)
tokenized_a = utils.tokenize_utterance(a_utterance)
# throw this conversation out if difference in utterance length is greater than 20
if abs(len(tokenized_b) - len(tokenized_a)) >= 20:
return None
coordination_counts = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
for marker_id in range(0, 8):
if (a_utter_vec[marker_id] > 0) and (b_utter_vec[marker_id] > 0):
coordination_counts[marker_id] = coordination_counts[marker_id] + 1.0
return coordination_counts
def get_liwc_features(combined_utterances):
return list(utils.get_liwc_counts_from_utterance(combined_utterances))
