sepehr/chartbastan
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Initial commit

Browse Source
This commit is contained in:
sepehr
2026-02-01 09:31:38 +01:00
commit e02db93960
4396 changed files with 1511612 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
backend/venv/Lib/site-packages/vaderSentiment/__init__.py Normal file
View File

@@ -0,0 +1 @@
#!/usr/bin/python

3570
backend/venv/Lib/site-packages/vaderSentiment/emoji_utf8_lexicon.txt Normal file
View File

File diff suppressed because it is too large Load Diff

687
backend/venv/Lib/site-packages/vaderSentiment/vaderSentiment.py Normal file
View File

@@ -0,0 +1,687 @@
# coding: utf-8
# Author: C.J. Hutto
# Thanks to George Berry for reducing the time complexity from something like O(N^4) to O(N).
# Thanks to Ewan Klein and Pierpaolo Pantone for bringing VADER into NLTK. Those modifications were awesome.
# For license information, see LICENSE.TXT
"""
If you use the VADER sentiment analysis tools, please cite:
Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for
Sentiment Analysis of Social Media Text. Eighth International Conference on
Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
"""
import os
import re
import math
import string
import codecs
import json
from itertools import product
from inspect import getsourcefile
from io import open
# ##Constants##
# (empirically derived mean sentiment intensity rating increase for booster words)
B_INCR = 0.293
B_DECR = -0.293
# (empirically derived mean sentiment intensity rating increase for using ALLCAPs to emphasize a word)
C_INCR = 0.733
N_SCALAR = -0.74
NEGATE = \
["aint", "arent", "cannot", "cant", "couldnt", "darent", "didnt", "doesnt",
"ain't", "aren't", "can't", "couldn't", "daren't", "didn't", "doesn't",
"dont", "hadnt", "hasnt", "havent", "isnt", "mightnt", "mustnt", "neither",
"don't", "hadn't", "hasn't", "haven't", "isn't", "mightn't", "mustn't",
"neednt", "needn't", "never", "none", "nope", "nor", "not", "nothing", "nowhere",
"oughtnt", "shant", "shouldnt", "uhuh", "wasnt", "werent",
"oughtn't", "shan't", "shouldn't", "uh-uh", "wasn't", "weren't",
"without", "wont", "wouldnt", "won't", "wouldn't", "rarely", "seldom", "despite"]
# booster/dampener 'intensifiers' or 'degree adverbs'
# http://en.wiktionary.org/wiki/Category:English_degree_adverbs
BOOSTER_DICT = \
{"absolutely": B_INCR, "amazingly": B_INCR, "awfully": B_INCR,
"completely": B_INCR, "considerable": B_INCR, "considerably": B_INCR,
"decidedly": B_INCR, "deeply": B_INCR, "effing": B_INCR, "enormous": B_INCR, "enormously": B_INCR,
"entirely": B_INCR, "especially": B_INCR, "exceptional": B_INCR, "exceptionally": B_INCR,
"extreme": B_INCR, "extremely": B_INCR,
"fabulously": B_INCR, "flipping": B_INCR, "flippin": B_INCR, "frackin": B_INCR, "fracking": B_INCR,
"fricking": B_INCR, "frickin": B_INCR, "frigging": B_INCR, "friggin": B_INCR, "fully": B_INCR,
"fuckin": B_INCR, "fucking": B_INCR, "fuggin": B_INCR, "fugging": B_INCR,
"greatly": B_INCR, "hella": B_INCR, "highly": B_INCR, "hugely": B_INCR,
"incredible": B_INCR, "incredibly": B_INCR, "intensely": B_INCR,
"major": B_INCR, "majorly": B_INCR, "more": B_INCR, "most": B_INCR, "particularly": B_INCR,
"purely": B_INCR, "quite": B_INCR, "really": B_INCR, "remarkably": B_INCR,
"so": B_INCR, "substantially": B_INCR,
"thoroughly": B_INCR, "total": B_INCR, "totally": B_INCR, "tremendous": B_INCR, "tremendously": B_INCR,
"uber": B_INCR, "unbelievably": B_INCR, "unusually": B_INCR, "utter": B_INCR, "utterly": B_INCR,
"very": B_INCR,
"almost": B_DECR, "barely": B_DECR, "hardly": B_DECR, "just enough": B_DECR,
"kind of": B_DECR, "kinda": B_DECR, "kindof": B_DECR, "kind-of": B_DECR,
"less": B_DECR, "little": B_DECR, "marginal": B_DECR, "marginally": B_DECR,
"occasional": B_DECR, "occasionally": B_DECR, "partly": B_DECR,
"scarce": B_DECR, "scarcely": B_DECR, "slight": B_DECR, "slightly": B_DECR, "somewhat": B_DECR,
"sort of": B_DECR, "sorta": B_DECR, "sortof": B_DECR, "sort-of": B_DECR}
# check for sentiment laden idioms that do not contain lexicon words (future work, not yet implemented)
SENTIMENT_LADEN_IDIOMS = {"cut the mustard": 2, "hand to mouth": -2,
"back handed": -2, "blow smoke": -2, "blowing smoke": -2,
"upper hand": 1, "break a leg": 2,
"cooking with gas": 2, "in the black": 2, "in the red": -2,
"on the ball": 2, "under the weather": -2}
# check for special case idioms and phrases containing lexicon words
SPECIAL_CASES = {"the shit": 3, "the bomb": 3, "bad ass": 1.5, "badass": 1.5, "bus stop": 0.0,
"yeah right": -2, "kiss of death": -1.5, "to die for": 3, "beating heart": 3.5}
# #Static methods# #
def negated(input_words, include_nt=True):
"""
Determine if input contains negation words
"""
input_words = [str(w).lower() for w in input_words]
neg_words = []
neg_words.extend(NEGATE)
for word in neg_words:
if word in input_words:
return True
if include_nt:
for word in input_words:
if "n't" in word:
return True
'''if "least" in input_words:
i = input_words.index("least")
if i > 0 and input_words[i - 1] != "at":
return True'''
return False
def normalize(score, alpha=15):
"""
Normalize the score to be between -1 and 1 using an alpha that
approximates the max expected value
"""
norm_score = score / math.sqrt((score * score) + alpha)
if norm_score < -1.0:
return -1.0
elif norm_score > 1.0:
return 1.0
else:
return norm_score
def allcap_differential(words):
"""
Check whether just some words in the input are ALL CAPS
:param list words: The words to inspect
:returns: `True` if some but not all items in `words` are ALL CAPS
"""
is_different = False
allcap_words = 0
for word in words:
if word.isupper():
allcap_words += 1
cap_differential = len(words) - allcap_words
if 0 < cap_differential < len(words):
is_different = True
return is_different
def scalar_inc_dec(word, valence, is_cap_diff):
"""
Check if the preceding words increase, decrease, or negate/nullify the
valence
"""
scalar = 0.0
word_lower = word.lower()
if word_lower in BOOSTER_DICT:
scalar = BOOSTER_DICT[word_lower]
if valence < 0:
scalar *= -1
# check if booster/dampener word is in ALLCAPS (while others aren't)
if word.isupper() and is_cap_diff:
if valence > 0:
scalar += C_INCR
else:
scalar -= C_INCR
return scalar
class SentiText(object):
"""
Identify sentiment-relevant string-level properties of input text.
"""
def __init__(self, text):
if not isinstance(text, str):
text = str(text).encode('utf-8')
self.text = text
self.words_and_emoticons = self._words_and_emoticons()
# doesn't separate words from\
# adjacent punctuation (keeps emoticons & contractions)
self.is_cap_diff = allcap_differential(self.words_and_emoticons)
@staticmethod
def _strip_punc_if_word(token):
"""
Removes all trailing and leading punctuation
If the resulting string has two or fewer characters,
then it was likely an emoticon, so return original string
(ie ":)" stripped would be "", so just return ":)"
"""
stripped = token.strip(string.punctuation)
if len(stripped) <= 2:
return token
return stripped
def _words_and_emoticons(self):
"""
Removes leading and trailing puncutation
Leaves contractions and most emoticons
Does not preserve punc-plus-letter emoticons (e.g. :D)
"""
wes = self.text.split()
stripped = list(map(self._strip_punc_if_word, wes))
return stripped
class SentimentIntensityAnalyzer(object):
"""
Give a sentiment intensity score to sentences.
"""
def __init__(self, lexicon_file="vader_lexicon.txt", emoji_lexicon="emoji_utf8_lexicon.txt"):
_this_module_file_path_ = os.path.abspath(getsourcefile(lambda: 0))
lexicon_full_filepath = os.path.join(os.path.dirname(_this_module_file_path_), lexicon_file)
with codecs.open(lexicon_full_filepath, encoding='utf-8') as f:
self.lexicon_full_filepath = f.read()
self.lexicon = self.make_lex_dict()
emoji_full_filepath = os.path.join(os.path.dirname(_this_module_file_path_), emoji_lexicon)
with codecs.open(emoji_full_filepath, encoding='utf-8') as f:
self.emoji_full_filepath = f.read()
self.emojis = self.make_emoji_dict()
def make_lex_dict(self):
"""
Convert lexicon file to a dictionary
"""
lex_dict = {}
for line in self.lexicon_full_filepath.rstrip('\n').split('\n'):
if not line:
continue
(word, measure) = line.strip().split('\t')[0:2]
lex_dict[word] = float(measure)
return lex_dict
def make_emoji_dict(self):
"""
Convert emoji lexicon file to a dictionary
"""
emoji_dict = {}
for line in self.emoji_full_filepath.rstrip('\n').split('\n'):
(emoji, description) = line.strip().split('\t')[0:2]
emoji_dict[emoji] = description
return emoji_dict
def polarity_scores(self, text):
"""
Return a float for sentiment strength based on the input text.
Positive values are positive valence, negative value are negative
valence.
"""
# convert emojis to their textual descriptions
text_no_emoji = ""
prev_space = True
for chr in text:
if chr in self.emojis:
# get the textual description
description = self.emojis[chr]
if not prev_space:
text_no_emoji += ' '
text_no_emoji += description
prev_space = False
else:
text_no_emoji += chr
prev_space = chr == ' '
text = text_no_emoji.strip()
sentitext = SentiText(text)
sentiments = []
words_and_emoticons = sentitext.words_and_emoticons
for i, item in enumerate(words_and_emoticons):
valence = 0
# check for vader_lexicon words that may be used as modifiers or negations
if item.lower() in BOOSTER_DICT:
sentiments.append(valence)
continue
if (i < len(words_and_emoticons) - 1 and item.lower() == "kind" and
words_and_emoticons[i + 1].lower() == "of"):
sentiments.append(valence)
continue
sentiments = self.sentiment_valence(valence, sentitext, item, i, sentiments)
sentiments = self._but_check(words_and_emoticons, sentiments)
valence_dict = self.score_valence(sentiments, text)
return valence_dict
def sentiment_valence(self, valence, sentitext, item, i, sentiments):
is_cap_diff = sentitext.is_cap_diff
words_and_emoticons = sentitext.words_and_emoticons
item_lowercase = item.lower()
if item_lowercase in self.lexicon:
# get the sentiment valence
valence = self.lexicon[item_lowercase]
# check for "no" as negation for an adjacent lexicon item vs "no" as its own stand-alone lexicon item
if item_lowercase == "no" and i != len(words_and_emoticons)-1 and words_and_emoticons[i + 1].lower() in self.lexicon:
# don't use valence of "no" as a lexicon item. Instead set it's valence to 0.0 and negate the next item
valence = 0.0
if (i > 0 and words_and_emoticons[i - 1].lower() == "no") \
or (i > 1 and words_and_emoticons[i - 2].lower() == "no") \
or (i > 2 and words_and_emoticons[i - 3].lower() == "no" and words_and_emoticons[i - 1].lower() in ["or", "nor"] ):
valence = self.lexicon[item_lowercase] * N_SCALAR
# check if sentiment laden word is in ALL CAPS (while others aren't)
if item.isupper() and is_cap_diff:
if valence > 0:
valence += C_INCR
else:
valence -= C_INCR
for start_i in range(0, 3):
# dampen the scalar modifier of preceding words and emoticons
# (excluding the ones that immediately preceed the item) based
# on their distance from the current item.
if i > start_i and words_and_emoticons[i - (start_i + 1)].lower() not in self.lexicon:
s = scalar_inc_dec(words_and_emoticons[i - (start_i + 1)], valence, is_cap_diff)
if start_i == 1 and s != 0:
s = s * 0.95
if start_i == 2 and s != 0:
s = s * 0.9
valence = valence + s
valence = self._negation_check(valence, words_and_emoticons, start_i, i)
if start_i == 2:
valence = self._special_idioms_check(valence, words_and_emoticons, i)
valence = self._least_check(valence, words_and_emoticons, i)
sentiments.append(valence)
return sentiments
def _least_check(self, valence, words_and_emoticons, i):
# check for negation case using "least"
if i > 1 and words_and_emoticons[i - 1].lower() not in self.lexicon \
and words_and_emoticons[i - 1].lower() == "least":
if words_and_emoticons[i - 2].lower() != "at" and words_and_emoticons[i - 2].lower() != "very":
valence = valence * N_SCALAR
elif i > 0 and words_and_emoticons[i - 1].lower() not in self.lexicon \
and words_and_emoticons[i - 1].lower() == "least":
valence = valence * N_SCALAR
return valence
@staticmethod
def _but_check(words_and_emoticons, sentiments):
# check for modification in sentiment due to contrastive conjunction 'but'
words_and_emoticons_lower = [str(w).lower() for w in words_and_emoticons]
if 'but' in words_and_emoticons_lower:
bi = words_and_emoticons_lower.index('but')
for sentiment in sentiments:
si = sentiments.index(sentiment)
if si < bi:
sentiments.pop(si)
sentiments.insert(si, sentiment * 0.5)
elif si > bi:
sentiments.pop(si)
sentiments.insert(si, sentiment * 1.5)
return sentiments
@staticmethod
def _special_idioms_check(valence, words_and_emoticons, i):
words_and_emoticons_lower = [str(w).lower() for w in words_and_emoticons]
onezero = "{0} {1}".format(words_and_emoticons_lower[i - 1], words_and_emoticons_lower[i])
twoonezero = "{0} {1} {2}".format(words_and_emoticons_lower[i - 2],
words_and_emoticons_lower[i - 1], words_and_emoticons_lower[i])
twoone = "{0} {1}".format(words_and_emoticons_lower[i - 2], words_and_emoticons_lower[i - 1])
threetwoone = "{0} {1} {2}".format(words_and_emoticons_lower[i - 3],
words_and_emoticons_lower[i - 2], words_and_emoticons_lower[i - 1])
threetwo = "{0} {1}".format(words_and_emoticons_lower[i - 3], words_and_emoticons_lower[i - 2])
sequences = [onezero, twoonezero, twoone, threetwoone, threetwo]
for seq in sequences:
if seq in SPECIAL_CASES:
valence = SPECIAL_CASES[seq]
break
if len(words_and_emoticons_lower) - 1 > i:
zeroone = "{0} {1}".format(words_and_emoticons_lower[i], words_and_emoticons_lower[i + 1])
if zeroone in SPECIAL_CASES:
valence = SPECIAL_CASES[zeroone]
if len(words_and_emoticons_lower) - 1 > i + 1:
zeroonetwo = "{0} {1} {2}".format(words_and_emoticons_lower[i], words_and_emoticons_lower[i + 1],
words_and_emoticons_lower[i + 2])
if zeroonetwo in SPECIAL_CASES:
valence = SPECIAL_CASES[zeroonetwo]
# check for booster/dampener bi-grams such as 'sort of' or 'kind of'
n_grams = [threetwoone, threetwo, twoone]
for n_gram in n_grams:
if n_gram in BOOSTER_DICT:
valence = valence + BOOSTER_DICT[n_gram]
return valence
@staticmethod
def _sentiment_laden_idioms_check(valence, senti_text_lower):
# Future Work
# check for sentiment laden idioms that don't contain a lexicon word
idioms_valences = []
for idiom in SENTIMENT_LADEN_IDIOMS:
if idiom in senti_text_lower:
print(idiom, senti_text_lower)
valence = SENTIMENT_LADEN_IDIOMS[idiom]
idioms_valences.append(valence)
if len(idioms_valences) > 0:
valence = sum(idioms_valences) / float(len(idioms_valences))
return valence
@staticmethod
def _negation_check(valence, words_and_emoticons, start_i, i):
words_and_emoticons_lower = [str(w).lower() for w in words_and_emoticons]
if start_i == 0:
if negated([words_and_emoticons_lower[i - (start_i + 1)]]): # 1 word preceding lexicon word (w/o stopwords)
valence = valence * N_SCALAR
if start_i == 1:
if words_and_emoticons_lower[i - 2] == "never" and \
(words_and_emoticons_lower[i - 1] == "so" or
words_and_emoticons_lower[i - 1] == "this"):
valence = valence * 1.25
elif words_and_emoticons_lower[i - 2] == "without" and \
words_and_emoticons_lower[i - 1] == "doubt":
valence = valence
elif negated([words_and_emoticons_lower[i - (start_i + 1)]]): # 2 words preceding the lexicon word position
valence = valence * N_SCALAR
if start_i == 2:
if words_and_emoticons_lower[i - 3] == "never" and \
(words_and_emoticons_lower[i - 2] == "so" or words_and_emoticons_lower[i - 2] == "this") or \
(words_and_emoticons_lower[i - 1] == "so" or words_and_emoticons_lower[i - 1] == "this"):
valence = valence * 1.25
elif words_and_emoticons_lower[i - 3] == "without" and \
(words_and_emoticons_lower[i - 2] == "doubt" or words_and_emoticons_lower[i - 1] == "doubt"):
valence = valence
elif negated([words_and_emoticons_lower[i - (start_i + 1)]]): # 3 words preceding the lexicon word position
valence = valence * N_SCALAR
return valence
def _punctuation_emphasis(self, text):
# add emphasis from exclamation points and question marks
ep_amplifier = self._amplify_ep(text)
qm_amplifier = self._amplify_qm(text)
punct_emph_amplifier = ep_amplifier + qm_amplifier
return punct_emph_amplifier
@staticmethod
def _amplify_ep(text):
# check for added emphasis resulting from exclamation points (up to 4 of them)
ep_count = text.count("!")
if ep_count > 4:
ep_count = 4
# (empirically derived mean sentiment intensity rating increase for
# exclamation points)
ep_amplifier = ep_count * 0.292
return ep_amplifier
@staticmethod
def _amplify_qm(text):
# check for added emphasis resulting from question marks (2 or 3+)
qm_count = text.count("?")
qm_amplifier = 0
if qm_count > 1:
if qm_count <= 3:
# (empirically derived mean sentiment intensity rating increase for
# question marks)
qm_amplifier = qm_count * 0.18
else:
qm_amplifier = 0.96
return qm_amplifier
@staticmethod
def _sift_sentiment_scores(sentiments):
# want separate positive versus negative sentiment scores
pos_sum = 0.0
neg_sum = 0.0
neu_count = 0
for sentiment_score in sentiments:
if sentiment_score > 0:
pos_sum += (float(sentiment_score) + 1) # compensates for neutral words that are counted as 1
if sentiment_score < 0:
neg_sum += (float(sentiment_score) - 1) # when used with math.fabs(), compensates for neutrals
if sentiment_score == 0:
neu_count += 1
return pos_sum, neg_sum, neu_count
def score_valence(self, sentiments, text):
if sentiments:
sum_s = float(sum(sentiments))
# compute and add emphasis from punctuation in text
punct_emph_amplifier = self._punctuation_emphasis(text)
if sum_s > 0:
sum_s += punct_emph_amplifier
elif sum_s < 0:
sum_s -= punct_emph_amplifier
compound = normalize(sum_s)
# discriminate between positive, negative and neutral sentiment scores
pos_sum, neg_sum, neu_count = self._sift_sentiment_scores(sentiments)
if pos_sum > math.fabs(neg_sum):
pos_sum += punct_emph_amplifier
elif pos_sum < math.fabs(neg_sum):
neg_sum -= punct_emph_amplifier
total = pos_sum + math.fabs(neg_sum) + neu_count
pos = math.fabs(pos_sum / total)
neg = math.fabs(neg_sum / total)
neu = math.fabs(neu_count / total)
else:
compound = 0.0
pos = 0.0
neg = 0.0
neu = 0.0
sentiment_dict = \
{"neg": round(neg, 3),
"neu": round(neu, 3),
"pos": round(pos, 3),
"compound": round(compound, 4)}
return sentiment_dict
if __name__ == '__main__':
# --- examples -------
sentences = ["VADER is smart, handsome, and funny.", # positive sentence example
"VADER is smart, handsome, and funny!",
# punctuation emphasis handled correctly (sentiment intensity adjusted)
"VADER is very smart, handsome, and funny.",
# booster words handled correctly (sentiment intensity adjusted)
"VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled
"VADER is VERY SMART, handsome, and FUNNY!!!",
# combination of signals - VADER appropriately adjusts intensity
"VADER is VERY SMART, uber handsome, and FRIGGIN FUNNY!!!",
# booster words & punctuation make this close to ceiling for score
"VADER is not smart, handsome, nor funny.", # negation sentence example
"The book was good.", # positive sentence
"At least it isn't a horrible book.", # negated negative sentence with contraction
"The book was only kind of good.",
# qualified positive sentence is handled correctly (intensity adjusted)
"The plot was good, but the characters are uncompelling and the dialog is not great.",
# mixed negation sentence
"Today SUX!", # negative slang with capitalization emphasis
"Today only kinda sux! But I'll get by, lol",
# mixed sentiment example with slang and constrastive conjunction "but"
"Make sure you :) or :D today!", # emoticons handled
"Catch utf-8 emoji such as 💘 and 💋 and 😁", # emojis handled
"Not bad at all" # Capitalized negation
]
analyzer = SentimentIntensityAnalyzer()
print("----------------------------------------------------")
print(" - Analyze typical example cases, including handling of:")
print(" -- negations")
print(" -- punctuation emphasis & punctuation flooding")
print(" -- word-shape as emphasis (capitalization difference)")
print(" -- degree modifiers (intensifiers such as 'very' and dampeners such as 'kind of')")
print(" -- slang words as modifiers such as 'uber' or 'friggin' or 'kinda'")
print(" -- contrastive conjunction 'but' indicating a shift in sentiment; sentiment of later text is dominant")
print(" -- use of contractions as negations")
print(" -- sentiment laden emoticons such as :) and :D")
print(" -- utf-8 encoded emojis such as 💘 and 💋 and 😁")
print(" -- sentiment laden slang words (e.g., 'sux')")
print(" -- sentiment laden initialisms and acronyms (for example: 'lol') \n")
for sentence in sentences:
vs = analyzer.polarity_scores(sentence)
print("{:-<65} {}".format(sentence, str(vs)))
print("----------------------------------------------------")
print(" - About the scoring: ")
print(""" -- The 'compound' score is computed by summing the valence scores of each word in the lexicon, adjusted
according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive).
This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence.
Calling it a 'normalized, weighted composite score' is accurate.""")
print(""" -- The 'pos', 'neu', and 'neg' scores are ratios for proportions of text that fall in each category (so these
should all add up to be 1... or close to it with float operation). These are the most useful metrics if
you want multidimensional measures of sentiment for a given sentence.""")
print("----------------------------------------------------")
# input("\nPress Enter to continue the demo...\n") # for DEMO purposes...
tricky_sentences = ["Sentiment analysis has never been good.",
"Sentiment analysis has never been this good!",
"Most automated sentiment analysis tools are shit.",
"With VADER, sentiment analysis is the shit!",
"Other sentiment analysis tools can be quite bad.",
"On the other hand, VADER is quite bad ass",
"VADER is such a badass!", # slang with punctuation emphasis
"Without a doubt, excellent idea.",
"Roger Dodger is one of the most compelling variations on this theme.",
"Roger Dodger is at least compelling as a variation on the theme.",
"Roger Dodger is one of the least compelling variations on this theme.",
"Not such a badass after all.", # Capitalized negation with slang
"Without a doubt, an excellent idea." # "without {any} doubt" as negation
]
print("----------------------------------------------------")
print(" - Analyze examples of tricky sentences that cause trouble to other sentiment analysis tools.")
print(" -- special case idioms - e.g., 'never good' vs 'never this good', or 'bad' vs 'bad ass'.")
print(" -- special uses of 'least' as negation versus comparison \n")
for sentence in tricky_sentences:
vs = analyzer.polarity_scores(sentence)
print("{:-<69} {}".format(sentence, str(vs)))
print("----------------------------------------------------")
# input("\nPress Enter to continue the demo...\n") # for DEMO purposes...
print("----------------------------------------------------")
print(
" - VADER works best when analysis is done at the sentence level (but it can work on single words or entire novels).")
paragraph = "It was one of the worst movies I've seen, despite good reviews. Unbelievably bad acting!! Poor direction. VERY poor production. The movie was bad. Very bad movie. VERY BAD movie!"
print(" -- For example, given the following paragraph text from a hypothetical movie review:\n\t'{}'".format(
paragraph))
print(
" -- You could use NLTK to break the paragraph into sentence tokens for VADER, then average the results for the paragraph like this: \n")
# simple example to tokenize paragraph into sentences for VADER
from nltk import tokenize
sentence_list = tokenize.sent_tokenize(paragraph)
paragraphSentiments = 0.0
for sentence in sentence_list:
vs = analyzer.polarity_scores(sentence)
print("{:-<69} {}".format(sentence, str(vs["compound"])))
paragraphSentiments += vs["compound"]
print("AVERAGE SENTIMENT FOR PARAGRAPH: \t" + str(round(paragraphSentiments / len(sentence_list), 4)))
print("----------------------------------------------------")
# input("\nPress Enter to continue the demo...\n") # for DEMO purposes...
print("----------------------------------------------------")
print(" - Analyze sentiment of IMAGES/VIDEO data based on annotation 'tags' or image labels. \n")
conceptList = ["balloons", "cake", "candles", "happy birthday", "friends", "laughing", "smiling", "party"]
conceptSentiments = 0.0
for concept in conceptList:
vs = analyzer.polarity_scores(concept)
print("{:-<15} {}".format(concept, str(vs['compound'])))
conceptSentiments += vs["compound"]
print("AVERAGE SENTIMENT OF TAGS/LABELS: \t" + str(round(conceptSentiments / len(conceptList), 4)))
print("\t")
conceptList = ["riot", "fire", "fight", "blood", "mob", "war", "police", "tear gas"]
conceptSentiments = 0.0
for concept in conceptList:
vs = analyzer.polarity_scores(concept)
print("{:-<15} {}".format(concept, str(vs['compound'])))
conceptSentiments += vs["compound"]
print("AVERAGE SENTIMENT OF TAGS/LABELS: \t" + str(round(conceptSentiments / len(conceptList), 4)))
print("----------------------------------------------------")
# input("\nPress Enter to continue the demo...") # for DEMO purposes...
do_translate = input(
"\nWould you like to run VADER demo examples with NON-ENGLISH text? \n (Note: requires Internet access and uses the 'requests' library) \n Type 'y' or 'n', then press Enter: ")
if do_translate.lower().lstrip().__contains__("y"):
import requests
print("\n----------------------------------------------------")
print(" - Analyze sentiment of NON ENGLISH text...for example:")
print(" -- French, German, Spanish, Italian, Russian, Japanese, Arabic, Chinese(Simplified) , Chinese(Traditional)")
print(" -- many other languages supported. \n")
languages = ["English", "French", "German", "Spanish", "Italian", "Russian", "Japanese", "Arabic", "Chinese(Simplified)", "Chinese(Traditional)"]
language_codes = ["en", "fr", "de", "es", "it", "ru", "ja", "ar", "zh-CN", "zh-TW"]
nonEnglish_sentences = ["I'm surprised to see just how amazingly helpful VADER is!",
"Je suis surpris de voir comment VADER est incroyablement utile !",
"Ich bin überrascht zu sehen, nur wie erstaunlich nützlich VADER!",
"Me sorprende ver sólo cómo increíblemente útil VADER!",
"Sono sorpreso di vedere solo come incredibilmente utile VADER è!",
"Я удивлен увидеть, как раз как удивительно полезно ВЕЙДЕРА!",
"私はちょうどどのように驚くほど役に立つベイダーを見て驚いています!",
"أنا مندهش لرؤية فقط كيف مثير للدهشة فيدر فائدة!",
"我很惊讶地看到VADER是如此有用!",
"我很驚訝地看到VADER是如此有用!"
]
for sentence in nonEnglish_sentences:
to_lang = "en"
from_lang = language_codes[nonEnglish_sentences.index(sentence)]
if (from_lang == "en") or (from_lang == "en-US"):
translation = sentence
translator_name = "No translation needed"
else: # please note usage limits for My Memory Translation Service: http://mymemory.translated.net/doc/usagelimits.php
# using MY MEMORY NET http://mymemory.translated.net
api_url = "http://mymemory.translated.net/api/get?q={}&langpair={}|{}".format(sentence, from_lang,
to_lang)
hdrs = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
'Accept-Encoding': 'none',
'Accept-Language': 'en-US,en;q=0.8',
'Connection': 'keep-alive'}
response = requests.get(api_url, headers=hdrs)
response_json = json.loads(response.text)
translation = response_json["responseData"]["translatedText"]
translator_name = "MemoryNet Translation Service"
vs = analyzer.polarity_scores(translation)
print("- {: <8}: {: <69}\t {} ({})".format(languages[nonEnglish_sentences.index(sentence)], sentence,
str(vs['compound']), translator_name))
print("----------------------------------------------------")
print("\n\n Demo Done!")

7520
backend/venv/Lib/site-packages/vaderSentiment/vader_lexicon.txt Normal file
View File

File diff suppressed because it is too large Load Diff