def read_training_data(file_name):
"""
read kaf/naf and matches the aspects with the words
"""
parser = KafNafParser(PATH_ANNOTATED_DATA + file_name)
terms = list(parser.get_terms())
# create token dictionairy containing naf info
tokens_container = dict()
for token_el in parser.get_tokens():
token_node = token_el.node
token_id = token_node.get('wid').replace('w', 't')
token_info = token_node.attrib
tokens_container[token_id] = token_info
properties = list(parser.get_properties())
handled_properties, term_dict = handle_properties(properties, terms,
tokens_container)
return terms, properties, handled_properties, term_dict, tokens_container
def process_file(this_file,token_freq):
xml_obj = KafNafParser(this_file)
print>>sys.stderr,'Processing file',this_file
token_for_wid = {}
order_for_wid = {}
opinion_expressions = []
opinion_targets = []
whole_text = ' '
for n, token in enumerate(xml_obj.get_tokens()):
text = token.get_text().lower()
token_freq[text] += 1
token_for_wid[token.get_id()] = text
order_for_wid[token.get_id()] = n
whole_text += text + ' '
wids_for_tid = {}
lemma_for_wid = {}
pos_for_wid = {}
for term in xml_obj.get_terms():
tid = term.get_id()
wids = term.get_span().get_span_ids()
wids_for_tid[tid] = wids
for wid in wids:
lemma_for_wid[wid] = term.get_lemma()
pos_for_wid[wid] = term.get_pos()
##Properties!
aspects = [] ## [(label,term_span)...]
for property in xml_obj.get_properties():
for refs in property.get_references():
for span in refs:
aspects.append((property.get_type(),span.get_span_ids()))
already_counted = {EXP:set(), TAR:set()}
for opinion in xml_obj.get_opinions():
for this_type, opinion_obj in [(EXP,opinion.get_expression()),(TAR,opinion.get_target())]:
if this_type is EXP and opinion_obj.get_polarity()=='NON-OPINIONATED':
continue
if opinion_obj is not None:
span = opinion_obj.get_span()
if span is not None:
list_wids = []
for tid in span.get_span_ids():
list_wids.extend(wids_for_tid.get(tid,[]))
list_wids.sort(key=lambda wid: order_for_wid[wid]) ##Sorted according the the order of the tokens
string_wids = '#'.join(list_wids)
opinion_tokens = ' '.join( token_for_wid[wid] for wid in list_wids)
opinion_lemmas = ' '.join( lemma_for_wid[wid] for wid in list_wids)
opinion_pos = ' '.join( pos_for_wid[wid] for wid in list_wids)
if string_wids not in already_counted[this_type]:
if this_type == EXP:
polarity = (opinion_obj.get_polarity()).lower()
opinion_expressions.append((opinion_tokens,polarity,opinion_lemmas,opinion_pos))
else:
##Calculate the aspect type
possible_aspects = []
target_ids = span.get_span_ids()
for aspect_label, aspect_span in aspects:
num_in_common = len(set(target_ids) & set(aspect_span))
if num_in_common != 0:
possible_aspects.append((aspect_label,num_in_common,len(aspect_span)))
aspect_for_target = 'unknown'
if len(possible_aspects) != 0:
##Sorting by the number in common first, and by the lengtgh of the aspect secondly
aspect_for_target = sorted(possible_aspects,key=lambda t: (t[1],t[2]), reverse=True)[0][0]
opinion_targets.append((opinion_tokens,aspect_for_target, opinion_lemmas,opinion_pos))
already_counted[this_type].add(string_wids)
del xml_obj
print>>sys.stderr,'\tNumber of opinion expressions:',len(opinion_expressions)
print>>sys.stderr,'\tNumber of opinion targets:',len(opinion_targets)
print>>sys.stderr,'\tNumber of characters of the text:',len(whole_text)
return opinion_expressions, opinion_targets, whole_text
def process_file(this_file, token_freq):
xml_obj = KafNafParser(this_file)
print >> sys.stderr, 'Processing file', this_file
token_for_wid = {}
order_for_wid = {}
opinion_expressions = []
opinion_targets = []
whole_text = ' '
for n, token in enumerate(xml_obj.get_tokens()):
text = token.get_text().lower()
token_freq[text] += 1
token_for_wid[token.get_id()] = text
order_for_wid[token.get_id()] = n
whole_text += text + ' '
wids_for_tid = {}
lemma_for_wid = {}
pos_for_wid = {}
for term in xml_obj.get_terms():
tid = term.get_id()
wids = term.get_span().get_span_ids()
wids_for_tid[tid] = wids
for wid in wids:
lemma_for_wid[wid] = term.get_lemma()
pos_for_wid[wid] = term.get_pos()
##Properties!
aspects = [] ## [(label,term_span)...]
for property in xml_obj.get_properties():
for refs in property.get_references():
for span in refs:
aspects.append((property.get_type(), span.get_span_ids()))
already_counted = {EXP: set(), TAR: set()}
for opinion in xml_obj.get_opinions():
for this_type, opinion_obj in [(EXP, opinion.get_expression()),
(TAR, opinion.get_target())]:
if this_type is EXP and opinion_obj.get_polarity(
) == 'NON-OPINIONATED':
continue
if opinion_obj is not None:
span = opinion_obj.get_span()
if span is not None:
list_wids = []
for tid in span.get_span_ids():
list_wids.extend(wids_for_tid.get(tid, []))
list_wids.sort(key=lambda wid: order_for_wid[
wid]) ##Sorted according the the order of the tokens
string_wids = '#'.join(list_wids)
opinion_tokens = ' '.join(token_for_wid[wid]
for wid in list_wids)
opinion_lemmas = ' '.join(lemma_for_wid[wid]
for wid in list_wids)
opinion_pos = ' '.join(pos_for_wid[wid]
for wid in list_wids)
if string_wids not in already_counted[this_type]:
if this_type == EXP:
polarity = (opinion_obj.get_polarity()).lower()
opinion_expressions.append(
(opinion_tokens, polarity, opinion_lemmas,
opinion_pos))
else:
##Calculate the aspect type
possible_aspects = []
target_ids = span.get_span_ids()
for aspect_label, aspect_span in aspects:
num_in_common = len(
set(target_ids) & set(aspect_span))
if num_in_common != 0:
possible_aspects.append(
(aspect_label, num_in_common,
len(aspect_span)))
aspect_for_target = 'unknown'
if len(possible_aspects) != 0:
##Sorting by the number in common first, and by the lengtgh of the aspect secondly
aspect_for_target = sorted(possible_aspects,
key=lambda t:
(t[1], t[2]),
reverse=True)[0][0]
opinion_targets.append(
(opinion_tokens, aspect_for_target,
opinion_lemmas, opinion_pos))
already_counted[this_type].add(string_wids)
del xml_obj
print >> sys.stderr, '\tNumber of opinion expressions:', len(
opinion_expressions)
print >> sys.stderr, '\tNumber of opinion targets:', len(opinion_targets)
print >> sys.stderr, '\tNumber of characters of the text:', len(whole_text)
return opinion_expressions, opinion_targets, whole_text
