Source code for

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# Copyright 2017-2019 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
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import csv
import re
from abc import abstractmethod

from nlp_architect.models.absa.inference.data_types import SentimentDoc, SentimentSentence

[docs]class Anonymiser(object): """Abstract class for anonymiser algorithm, intended for privacy keeping."""
[docs] @abstractmethod def run(self, text): pass
[docs]class TweetAnonymiser(Anonymiser): """Anonymiser for tweets which uses lexicon for simple string replacements.""" def __init__(self, lexicon_path): self.entity_dict = self._init_entity_dict(lexicon_path) @staticmethod def _init_entity_dict(lexicon_path): ret = {} with open(lexicon_path, encoding='utf-8') as f: for row in csv.reader(f): ret[row[0]] = [_ for _ in row[1:] if _] return ret
[docs] def run(self, text): for anonymised, entities in self.entity_dict.items(): for entity in entities: text = re.sub(entity, anonymised, text, flags=re.IGNORECASE) text = ' '.join(["@other_entity" if (word.startswith('@') and word[1:] not in self.entity_dict.keys()) else word for word in text.split()]) return text
def _ui_format(sent: SentimentSentence, doc: SentimentDoc) -> str: """Get sentence as HTML with 4 classes: aspects, opinions, negations and intensifiers.""" text = doc.doc_text[sent.start: sent.end + 1] seen = set() for term in sorted([t for e in for t in e], key=lambda t: t.start)[::-1]: if term.start not in seen: seen.add(term.start) start = term.start - sent.start end = start + term.len label = term.type.value + '_' + term.polarity.value text = ''.join((text[:start], '<span class="', label, '">', text[start: end], '</span>', text[end:])) return text