def __init__(self):
self.splitter = Splitter()
self.postagger = POSTagger()
self.dicttagger = DictionaryTagger([
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/positive.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/negative.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/morePositive.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/moreNegative.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/invert.yml'
])
def processQuestion(gloveModel,
question,
minLen=1,
maxLen=3,
useAPI=False,
useSynonyms=False):
tagger = POSTagger()
pos = tagger.parse(question)
# create splitter and generalizer
splitter = Splitter()
if question[-1] == '?' or question[-1] == '.':
question = question[:-1]
gen_question = splitter.generalize(question, pos)
labels = []
resultsExists = False
if not useAPI:
parts = list(splitter.split(gen_question, min=minLen, max=maxLen))
else:
resultsExists = True
apiResult, _ = api.getBinaryRelations(question)
parts = [
rel.predicate for rel in apiResult
if len(rel.predicate_positions_) > 1
]
for part in parts:
if len(part.split()) > 1:
labels.append(part.split()[0] +
''.join(''.join([w[0].upper(), w[1:].lower()])
for w in part.split()[1:]))
if useSynonyms:
predicates = [max(part.split(), key=len) for part in parts]
if predicates is not None and len(predicates) > 0:
for predicate in predicates:
for part in list(parts):
if predicate in part:
for syn in gloveModel.gloveModel.most_similar(
predicate.lower()):
parts.append(part.replace(predicate, syn[0]))
if len(parts) == 0:
resultsExists = False
parts = list(splitter.split(gen_question, min=minLen, max=maxLen))
# create embedder part
vectors = []
for part in parts:
vectors.append(gloveModel.getVector(part))
return vectors, parts, pos, gen_question, labels, resultsExists
def __init__(self):
self.splitter = Splitter()
self.postagger = POSTagger()
self.dicttagger = DictionaryTagger(
[
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/positive.yml",
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/negative.yml",
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/morePositive.yml",
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/moreNegative.yml",
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/invert.yml",
]
)
class SentimentAnalyzingService(object):
def __init__(self):
self.splitter = Splitter()
self.postagger = POSTagger()
self.dicttagger = DictionaryTagger([
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/positive.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/negative.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/morePositive.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/moreNegative.yml',
'/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/invert.yml'
])
def valueOf(self, sentiment):
if sentiment == 'positive': return 1
if sentiment == 'negative': return -1
return 0
def sentence_score(self, sentence_tokens, previous_token, acum_score):
if not sentence_tokens:
return acum_score
else:
current_token = sentence_tokens[0]
tags = current_token[2]
token_score = sum([self.valueOf(tag) for tag in tags])
if previous_token is not None:
previous_tags = previous_token[2]
if 'inc' in previous_tags:
token_score *= 2.0
elif 'dec' in previous_tags:
token_score /= 2.0
elif 'inv' in previous_tags:
token_score *= -1.0
return self.sentence_score(sentence_tokens[1:], current_token,
acum_score + token_score)
def sentiment_score(self, dictTaggedSentences):
return sum([
self.sentence_score(sentence, None, 0.0)
for sentence in dictTaggedSentences
])
def performBasicSentimentAnalysis(self, textToBeAnalysed):
sentences = self.splitter.splitParagraphToListOfSentences(
textToBeAnalysed)
pos_tagged_sentences = self.postagger.pos_tag(sentences)
dict_tagged_sentences = self.dicttagger.tag(pos_tagged_sentences)
score = self.sentiment_score(dict_tagged_sentences)
return score
class SentimentAnalyzingService(object):
def __init__(self):
self.splitter = Splitter()
self.postagger = POSTagger()
self.dicttagger = DictionaryTagger(
[
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/positive.yml",
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/negative.yml",
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/morePositive.yml",
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/moreNegative.yml",
"/home/msinghal/PycharmProjects/basic_sentiment_analysis/dicts/invert.yml",
]
)
def valueOf(self, sentiment):
if sentiment == "positive":
return 1
if sentiment == "negative":
return -1
return 0
def sentence_score(self, sentence_tokens, previous_token, acum_score):
if not sentence_tokens:
return acum_score
else:
current_token = sentence_tokens[0]
tags = current_token[2]
token_score = sum([self.valueOf(tag) for tag in tags])
if previous_token is not None:
previous_tags = previous_token[2]
if "inc" in previous_tags:
token_score *= 2.0
elif "dec" in previous_tags:
token_score /= 2.0
elif "inv" in previous_tags:
token_score *= -1.0
return self.sentence_score(sentence_tokens[1:], current_token, acum_score + token_score)
def sentiment_score(self, dictTaggedSentences):
return sum([self.sentence_score(sentence, None, 0.0) for sentence in dictTaggedSentences])
def performBasicSentimentAnalysis(self, textToBeAnalysed):
sentences = self.splitter.splitParagraphToListOfSentences(textToBeAnalysed)
pos_tagged_sentences = self.postagger.pos_tag(sentences)
dict_tagged_sentences = self.dicttagger.tag(pos_tagged_sentences)
score = self.sentiment_score(dict_tagged_sentences)
return score
def extract_text(arg, punkt):
"""
Extract sentences from argument text and find the passive sentences
"""
with open(arg) as f:
logger.info("Reading Text file")
text = f.read()
sentences = punkt.tokenize(text)
logger.info(f"{len(sentences)} sentences detected")
for sent in sentences:
find_passives(sent)
print("-" * 60)
if __name__ == "__main__":
TAGGER = POSTagger().get()
if len(sys.argv) > 1:
# pre-trained version of PunktSentenceTokenizer
punkt = nltk.tokenize.punkt.PunktSentenceTokenizer()
for arg in sys.argv[1:]:
extract_text(arg, punkt)
else:
print("No sentences")
