# ******************************************************************************
# Copyright 2017-2018 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
from nlp_architect.models.absa.inference.data_types import Polarity
from nlp_architect.models.absa.train.data_types import OpinionTerm, QualifiedTerm
[docs]def set_opinion_term_polarity(terms_list):
"""Set opinion term polarity.
Args:
terms_list (list): list of opinion terms
"""
first = terms_list[0]
new_term = first.term
positive_pol = 0
negative_pol = 0
curr_polarity = None
for term in terms_list:
try:
curr_polarity = term.term_polarity
except Exception as e:
print("extracted_term missing term_polarity: " + str(e))
if curr_polarity is not None:
if curr_polarity == Polarity.POS:
positive_pol = positive_pol + 1
if curr_polarity == Polarity.NEG:
negative_pol = negative_pol + 1
new_term_polarity = Polarity.UNK
if positive_pol >= negative_pol and positive_pol > 0:
new_term_polarity = Polarity.POS
elif negative_pol >= positive_pol and negative_pol > 0:
new_term_polarity = Polarity.NEG
return OpinionTerm(new_term, new_term_polarity)
[docs]def generate_final_aspect_candidates_list(
aspect_candidate_list_raw, final_aspect_candidates_list, frequency_threshold
):
"""Generate final aspect candidates list from map.
Args:
aspect_candidate_list_raw (dict): key = term, value =
lists of candidate terms.
final_aspect_candidates_list (list): list of final aspect candidates
frequency_threshold (int): minimum freq. for qualifying term
"""
term_polarity = Polarity.UNK
for extracted_term_list in aspect_candidate_list_raw.values():
if len(extracted_term_list) >= frequency_threshold:
term = extracted_term_list[0]
qualified_term = QualifiedTerm(
term.term, term.lemma, term.pos, len(extracted_term_list), term_polarity
)
final_aspect_candidates_list.append(qualified_term)
return final_aspect_candidates_list
[docs]def generate_final_opinion_candidates_list(
opinion_candidate_list_raw, final_opinion_candidates_list, frequency_threshold
):
"""Generate final opinion candidates list from raw opinion candidate list.
Args:
opinion_candidate_list_raw (dict): key = term, value =
lists of extracted terms.
final_opinion_candidates_list (list): list of final opinion candidates
frequency_threshold (int): minimum freq. for qualifying term
"""
for extracted_term_list in opinion_candidate_list_raw.values():
positive_pol = 0
negative_pol = 0
if len(extracted_term_list) >= frequency_threshold:
for ex_term in extracted_term_list:
curr_polarity = ex_term.term_polarity
if curr_polarity is not None:
if curr_polarity == Polarity.POS:
positive_pol = positive_pol + 1
if curr_polarity == Polarity.NEG:
negative_pol = negative_pol + 1
# set polarity according majority vote
term_polarity = Polarity.UNK
if positive_pol > negative_pol and positive_pol > 0:
term_polarity = Polarity.POS
elif negative_pol >= positive_pol and negative_pol > 0:
term_polarity = Polarity.NEG
term = extracted_term_list[0]
qualified_term = QualifiedTerm(
term.term, term.term, term.pos, len(extracted_term_list), term_polarity
)
final_opinion_candidates_list.append(qualified_term)
return final_opinion_candidates_list