fixed import (#148)

* fixed import
* merged utils into sentiment sol
* deleted duplicate import

Author: danielkorat

solutions/absa_solution/sentiment_solution.py

@@ -22,13 +22,17 @@
 import numpy as np
 import pandas as pd
 from tqdm import tqdm
+import csv
+import re
+from abc import abstractmethod
 
 from nlp_architect import LIBRARY_OUT
 from nlp_architect.common.core_nlp_doc import CoreNLPDoc
 from nlp_architect.models.absa.inference.data_types import (
     TermType,
     SentimentDocEncoder,
     SentimentDoc,
+    SentimentSentence,
 )
 from nlp_architect.models.absa.inference.inference import SentimentInference
 from nlp_architect.models.absa.utils import load_opinion_lex
@@ -40,11 +44,62 @@
     line_count,
 )
 
-from .utils import Anonymiser, _ui_format
-
 SENTIMENT_OUT = LIBRARY_OUT / "absa_solution"
 
 
+class Anonymiser(object):
+    """Abstract class for anonymiser algorithm, intended for privacy keeping."""
+
+    @abstractmethod
+    def run(self, text):
+        pass
+
+
+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
+
+    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 sent.events 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
+
+
 class SentimentSolution(object):
     """Main class for executing Sentiment Solution pipeline.