def transform(self, corpus: Corpus) -> Corpus:
"""Computes the average number of questions asked in a conversation
:param corpus: the corpus to compute features for.
:type corpus: Corpus
"""
if self.verbose: print("Finding questions per utterance")
questions = []
allutterids = corpus.get_utterance_ids()
for i in list(range(0, len(allutterids))):
utter_id = allutterids[i]
text = corpus.get_utterance(utter_id).text
nquestions = len(re.findall(r'\?+', text))
questions.append(
nquestions) #gives number of questions in each utterance
if self.verbose: print("Finding questions per conversation")
allconvoids = corpus.get_conversation_ids()
for i in list(range(0, len(allconvoids))):
convo_id = allconvoids[i]
convo_utters = corpus.get_conversation(convo_id)._utterance_ids
avgquestion = np.mean(
np.asarray(questions)[np.asarray(convo_utters)])
corpus.get_conversation(convo_id)._meta[
self.ATTR_NAME] = avgquestion
#adds average questions per conversation to conversation metadata
return corpus
def transform(self, corpus: Corpus) -> Corpus:
"""Computes the count of pause and hesitancy words for each utterance, then aggregates them for each conversation
:param corpus: the corpus to compute features for.
:type corpus: Corpus
"""
if self.verbose:
print("Finding counts of pause and hesitancy words...")
pause_words = [
'um', 'umm', 'ummm', 'uh', 'uhh', 'uhhh', 'hm', 'hmm', 'hmmm',
'er', 'err', 'uh huh', 'huh', 'mhm', 'mhmm', 'erm', '...', 'ah',
'ahh', 'ahem', 'eh', 'ehh', 'ehhh', 'meh'
]
hesitant_words = [
'maybe', 'not', 'sure', 'unsure', 'probably', 'well', 'okay',
'like', 'actually', 'basically', 'seriously', 'totally',
'literally', 'know', 'mean', 'guess', 'suppose', 'but',
'something', 'so', 'wow', 'just', 'really', 'later', 'wait',
'future', 'almost', 'slightly', 'perhaps', 'somehow', 'sort',
'kind', 'little', 'somewhat', 'hey', 'alas', 'see', 'sounds', 'ok',
'roughly', 'why', 'how', 'yep', 'yup', 'may', 'possibly', 'might',
'could', 'doubt', 'skeptical', 'don\'t', 'won\'t', 'nah'
]
pause = []
hesitancy = []
allutterids = corpus.get_utterance_ids()
for i in list(range(0, len(allutterids))):
utter_id = allutterids[i]
text = corpus.get_utterance(utter_id).text
textcleaned = "".join(
c for c in text
if c not in ('!', '.', ':', '?', '\'', ',', '\"', '@', '#',
'$', '%', '^', '&', '*', '(', ')', '-', '~', '`',
'_', '+', '=', '>', '<', '[', ']', '{', '}'))
textlist = textcleaned.split()
npause = len([i for i in textlist if i in pause_words])
nhesitant = len([i for i in textlist if i in hesitant_words])
pause.append(
npause) #gives number of pause words in each utterance
hesitancy.append(
nhesitant) #gives number of hesitant words in each utterance
corpus.get_utterance(utter_id).meta[self.NAME1] = npause
corpus.get_utterance(utter_id).meta[self.NAME2] = nhesitant
allconvoids = corpus.get_conversation_ids()
for i in list(range(0, len(allconvoids))):
convo_id = allconvoids[i]
convo_utters = corpus.get_conversation(convo_id)._utterance_ids
avgpause = np.mean(np.asarray(pause)[np.asarray(convo_utters)])
avghesitancy = np.mean(
np.asarray(hesitancy)[np.asarray(convo_utters)])
corpus.get_conversation(convo_id)._meta[self.NAME3] = avgpause
corpus.get_conversation(convo_id)._meta[self.NAME4] = avghesitancy
return corpus
