def sent_result_subjectivity(text):
# Classify a single sentence as subjective/objective using a stored custom classifier.
word_tokenizer = regexp.WhitespaceTokenizer()
# Tokenize and convert to lower case
tokenized_text = [word.lower() for word in word_tokenizer.tokenize(text)]
sentim_analyzer = load('files/sa_subjectivity.pickle')
label = sentim_analyzer.classify(tokenized_text)
return label
def subjectivityScore(docs):
# Input: SpaCy doc class
# Output: Subjectivity score of document (percent of sentences considered subjective)
# Note: takes a few minutes the first time, to train classifier. After takes a few seconds.
word_tokenizer = regexp.WhitespaceTokenizer()
try:
sentiment = load('sa_subjectivity.pickle')
except LookupError:
sentiment = demo_subjectivity(NaiveBayesClassifier.train, True)
subjectivities = []
for doc in docs:
tot, subj = 0, 0
for sent in doc.sents:
tot += 1
# Tokenize and convert to lower case
tokenized = [word.lower() for word in word_tokenizer.tokenize(sent.text)]
if sentiment.classify(tokenized) == 'subj': subj += 1
subjectivities.append(subj / tot)
return subjectivities
def demo_sent_subjectivity(text):
"""
Classify a single sentence as subjective or objective using a stored
SentimentAnalyzer.
:param text: a sentence whose subjectivity has to be classified.
"""
from nltk.classify import NaiveBayesClassifier
from nltk.tokenize import regexp
word_tokenizer = regexp.WhitespaceTokenizer()
try:
sentim_analyzer = load('sa_subjectivity.pickle')
except LookupError:
print('Cannot find the sentiment analyzer you want to load.')
print('Training a new one using NaiveBayesClassifier.')
sentim_analyzer = demo_subjectivity(NaiveBayesClassifier.train, True)
# Tokenize and convert to lower case
tokenized_text = [word.lower() for word in word_tokenizer.tokenize(text)]
print(sentim_analyzer.classify(tokenized_text))
import json
import string
import re
import pandas as pd
import re
import json
import csv
import tokenize
import nltk
from nltk.tokenize import regexp
word_tokenizer = regexp.WhitespaceTokenizer()
docID = ""
index_tracker = {}
input_dict = {}
df = pd.DataFrame(columns=index_tracker.keys())
column_name = []
import yaml
# initialise
def find_column_name(input_dict, column_name):
if type(input_dict) == dict:
for key, value in input_dict.items():
if type(value) == dict:
if "find" in value.keys():
column_name.append(key)
if (key == "output"):
for i in value.keys():
column_name.append(i)
# print(key,column_name)
find_column_name(value, column_name)
