我一直在使用Vader Sentiment进行一些文本情绪分析,我注意到我的数据有很多“路要走”的短语被错误地归类为中性：
In[11]: sentiment('way to go John')
Out[11]: {'compound': 0.0,'neg': 0.0,'neu': 1.0,'pos': 0.0}
在深入了解Vader源代码后,我找到了以下字典：
# check for special case idioms using a sentiment-laden keyword known to SAGE
SPECIAL_CASE_IDIOMS = {"the shit": 3,"the bomb": 3,"bad ass": 1.5,"yeah right": -2,"cut the mustard": 2,"kiss of death": -1.5,"hand to mouth": -2,"way to go": 3}
如您所见,我手动添加了“方式去”条目.但是,似乎没有效果：
In [12]: sentiment('way to go John')
Out[12]: {'compound': 0.0,'pos': 0.0}
知道我错过了什么吗？或者更具体地说,我需要做些什么来添加自定义习语？这是Vader Sentiment源代码：
#######################################################################################################################
# SENTIMENT SCORING SCRIPT
#######################################################################################################################
'''
Created on July 04,2013
@author: C.J. Hutto
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,math,re,sys,fnmatch,string
reload(sys)
f = 'C:UsersjamacwanCodePythonTwitter APISentiment Analysisvader_sentiment_lexicon.txt'
def make_lex_dict(f):
return dict(map(lambda (w,m): (w,float(m)),[wmsr.strip().split('t')[0:2] for wmsr in open(f) ]))
WORD_VALENCE_DICT = make_lex_dict(f)
# empirically derived valence ratings for words,emoticons,slang,swear words,acronyms/initialisms
##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
# for removing punctuation
REGEX_REMOVE_PUNCTUATION = re.compile('[%s]' % re.escape(string.punctuation))
PUNC_LIST = [".","!","?",",";",":","-","'",""","!!","!!!","??","???","?!?","!?!","?!?!","!?!?"]
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,"considerably": B_INCR,"decidedly": B_INCR,"deeply": B_INCR,"effing": B_INCR,"enormously": B_INCR,"entirely": B_INCR,"especially": B_INCR,"exceptionally": B_INCR,"extremely": B_INCR,"fabulously": B_INCR,"flipping": B_INCR,"flippin": B_INCR,"fricking": B_INCR,"frickin": B_INCR,"frigging": B_INCR,"friggin": B_INCR,"fully": B_INCR,"fucking": B_INCR,"greatly": B_INCR,"hella": B_INCR,"highly": B_INCR,"hugely": B_INCR,"incredibly": B_INCR,"intensely": 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,"totally": B_INCR,"tremendously": B_INCR,"uber": B_INCR,"unbelievably": B_INCR,"unusually": 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,"marginally": B_DECR,"occasionally": B_DECR,"partly": B_DECR,"scarcely": B_DECR,"slightly": B_DECR,"somewhat": B_DECR,"sort of": B_DECR,"sorta": B_DECR,"sortof": B_DECR,"sort-of": B_DECR}
# check for special case idioms using a sentiment-laden keyword known to SAGE
SPECIAL_CASE_IDIOMS = {"the shit": 3,"way to go": 6}
def negated(list,nWords=[],includeNT=True):
nWords.extend(NEGATE)
for word in nWords:
if word in list:
return True
if includeNT:
for word in list:
if "n't" in word:
return True
if "least" in list:
i = list.index("least")
if i > 0 and list[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
normScore = score/math.sqrt( ((score*score) + alpha) )
return normScore
def wildCardMatch(patternWithWildcard,listOfStringsToMatchAgainst):
listOfMatches = fnmatch.filter(listOfStringsToMatchAgainst,patternWithWildcard)
return listOfMatches
def isALLCAP_differential(wordList):
countALLCAPS= 0
for w in wordList:
if w.isupper():
countALLCAPS += 1
cap_differential = len(wordList) - countALLCAPS
if cap_differential > 0 and cap_differential < len(wordList):
isDiff = True
else: isDiff = False
return isDiff
#check if the preceding words increase,decrease,or negate/nullify the valence
def scalar_inc_dec(word,valence,isCap_diff):
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 isCap_diff:
if valence > 0: scalar += c_INCR
else: scalar -= c_INCR
return scalar
def sentiment(text):
"""
Returns a float for sentiment strength based on the input text.
Positive values are positive valence,negative value are negative valence.
"""
if not isinstance(text,unicode) and not isinstance(text,str):
text = str(text)
wordsAndEmoticons = text.split() #doesn't separate words from adjacent punctuation (keeps emoticons & contractions)
text_mod = REGEX_REMOVE_PUNCTUATION.sub('',text) # removes punctuation (but loses emoticons & contractions)
wordsOnly = text_mod.split()
# get rid of empty items or single letter "words" like 'a' and 'I' from wordsOnly
for word in wordsOnly:
if len(word) <= 1:
wordsOnly.remove(word)
# now remove adjacent & redundant punctuation from [wordsAndEmoticons] while keeping emoticons and contractions
for word in wordsOnly:
for p in PUNC_LIST:
pword = p + word
x1 = wordsAndEmoticons.count(pword)
while x1 > 0:
i = wordsAndEmoticons.index(pword)
wordsAndEmoticons.remove(pword)
wordsAndEmoticons.insert(i,word)
x1 = wordsAndEmoticons.count(pword)
wordp = word + p
x2 = wordsAndEmoticons.count(wordp)
while x2 > 0:
i = wordsAndEmoticons.index(wordp)
wordsAndEmoticons.remove(wordp)
wordsAndEmoticons.insert(i,word)
x2 = wordsAndEmoticons.count(wordp)
# get rid of residual empty items or single letter "words" like 'a' and 'I' from wordsAndEmoticons
for word in wordsAndEmoticons:
if len(word) <= 1:
wordsAndEmoticons.remove(word)
# remove stopwords from [wordsAndEmoticons]
#stopwords = [str(word).strip() for word in open('stopwords.txt')]
#for word in wordsAndEmoticons:
# if word in stopwords:
# wordsAndEmoticons.remove(word)
# check for negation
isCap_diff = isALLCAP_differential(wordsAndEmoticons)
sentiments = []
for item in wordsAndEmoticons:
v = 0
i = wordsAndEmoticons.index(item)
if (i < len(wordsAndEmoticons)-1 and item.lower() == "kind" and
wordsAndEmoticons[i+1].lower() == "of") or item.lower() in BOOSTER_DICT:
sentiments.append(v)
continue
item_lowercase = item.lower()
if item_lowercase in WORD_VALENCE_DICT:
#get the sentiment valence
v = float(WORD_VALENCE_DICT[item_lowercase])
#check if sentiment laden word is in ALLCAPS (while others aren't)
if item.isupper() and isCap_diff:
if v > 0: v += c_INCR
else: v -= c_INCR
n_scalar = -0.74
if i > 0 and wordsAndEmoticons[i-1].lower() not in WORD_VALENCE_DICT:
s1 = scalar_inc_dec(wordsAndEmoticons[i-1],v,isCap_diff)
v = v+s1
if negated([wordsAndEmoticons[i-1]]): v = v*n_scalar
if i > 1 and wordsAndEmoticons[i-2].lower() not in WORD_VALENCE_DICT:
s2 = scalar_inc_dec(wordsAndEmoticons[i-2],isCap_diff)
if s2 != 0: s2 = s2*0.95
v = v+s2
# check for special use of 'never' as valence modifier instead of negation
if wordsAndEmoticons[i-2] == "never" and (wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"):
v = v*1.5
# otherwise,check for negation/nullification
elif negated([wordsAndEmoticons[i-2]]): v = v*n_scalar
if i > 2 and wordsAndEmoticons[i-3].lower() not in WORD_VALENCE_DICT:
s3 = scalar_inc_dec(wordsAndEmoticons[i-3],isCap_diff)
if s3 != 0: s3 = s3*0.9
v = v+s3
# check for special use of 'never' as valence modifier instead of negation
if wordsAndEmoticons[i-3] == "never" and
(wordsAndEmoticons[i-2] == "so" or wordsAndEmoticons[i-2] == "this") or
(wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"):
v = v*1.25
# otherwise,check for negation/nullification
elif negated([wordsAndEmoticons[i-3]]): v = v*n_scalar
# future work: consider other sentiment-laden idioms
#other_idioms = {"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}
onezero = u"{} {}".format(wordsAndEmoticons[i-1],wordsAndEmoticons[i])
twoonezero = u"{} {} {}".format(wordsAndEmoticons[i-2],wordsAndEmoticons[i-1],wordsAndEmoticons[i])
twoone = u"{} {}".format(wordsAndEmoticons[i-2],wordsAndEmoticons[i-1])
threetwoone = u"{} {} {}".format(wordsAndEmoticons[i-3],wordsAndEmoticons[i-2],wordsAndEmoticons[i-1])
threetwo = u"{} {}".format(wordsAndEmoticons[i-3],wordsAndEmoticons[i-2])
if onezero in SPECIAL_CASE_IDIOMS:
v = SPECIAL_CASE_IDIOMS[onezero]
elif twoonezero in SPECIAL_CASE_IDIOMS:
v = SPECIAL_CASE_IDIOMS[twoonezero]
elif twoone in SPECIAL_CASE_IDIOMS:
v = SPECIAL_CASE_IDIOMS[twoone]
elif threetwoone in SPECIAL_CASE_IDIOMS:
v = SPECIAL_CASE_IDIOMS[threetwoone]
elif threetwo in SPECIAL_CASE_IDIOMS:
v = SPECIAL_CASE_IDIOMS[threetwo]
if len(wordsAndEmoticons)-1 > i:
zeroone = u"{} {}".format(wordsAndEmoticons[i],wordsAndEmoticons[i+1])
if zeroone in SPECIAL_CASE_IDIOMS:
v = SPECIAL_CASE_IDIOMS[zeroone]
if len(wordsAndEmoticons)-1 > i+1:
zeroonetwo = u"{} {}".format(wordsAndEmoticons[i],wordsAndEmoticons[i+1],wordsAndEmoticons[i+2])
if zeroonetwo in SPECIAL_CASE_IDIOMS:
v = SPECIAL_CASE_IDIOMS[zeroonetwo]
# check for booster/dampener bi-grams such as 'sort of' or 'kind of'
if threetwo in BOOSTER_DICT or twoone in BOOSTER_DICT:
v = v+B_DECR
# check for negation case using "least"
if i > 1 and wordsAndEmoticons[i-1].lower() not in WORD_VALENCE_DICT
and wordsAndEmoticons[i-1].lower() == "least":
if (wordsAndEmoticons[i-2].lower() != "at" and wordsAndEmoticons[i-2].lower() != "very"):
v = v*n_scalar
elif i > 0 and wordsAndEmoticons[i-1].lower() not in WORD_VALENCE_DICT
and wordsAndEmoticons[i-1].lower() == "least":
v = v*n_scalar
sentiments.append(v)
# check for modification in sentiment due to contrastive conjunction 'but'
if 'but' in wordsAndEmoticons or 'BUT' in wordsAndEmoticons:
try: bi = wordsAndEmoticons.index('but')
except: bi = wordsAndEmoticons.index('BUT')
for s in sentiments:
si = sentiments.index(s)
if si < bi:
sentiments.pop(si)
sentiments.insert(si,s*0.5)
elif si > bi:
sentiments.pop(si)
sentiments.insert(si,s*1.5)
if sentiments:
sum_s = float(sum(sentiments))
#print sentiments,sum_s
# check for added emphasis resulting from exclamation points (up to 4 of them)
ep_count = text.count("!")
if ep_count > 4: ep_count = 4
ep_amplifier = ep_count*0.292 #(empirically derived mean sentiment intensity rating increase for exclamation points)
if sum_s > 0: sum_s += ep_amplifier
elif sum_s < 0: sum_s -= ep_amplifier
# 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: qm_amplifier = qm_count*0.18
else: qm_amplifier = 0.96
if sum_s > 0: sum_s += qm_amplifier
elif sum_s < 0: sum_s -= qm_amplifier
compound = normalize(sum_s)
# 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
if pos_sum > math.fabs(neg_sum): pos_sum += (ep_amplifier+qm_amplifier)
elif pos_sum < math.fabs(neg_sum): neg_sum -= (ep_amplifier+qm_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
s = {"neg" : round(neg,3),"neu" : round(neu,"pos" : round(pos,"compound" : round(compound,4)}
return s
if __name__ == '__main__':
# --- examples -------
sentences = [
u"VADER is smart,handsome,and funny.",# positive sentence example
u"VADER is smart,and funny!",# punctuation emphasis handled correctly (sentiment intensity adjusted)
u"VADER is very smart,# booster words handled correctly (sentiment intensity adjusted)
u"VADER is VERY SMART,and FUNNY.",# emphasis for ALLCAPS handled
u"VADER is VERY SMART,and FUNNY!!!",# combination of signals - VADER appropriately adjusts intensity
u"VADER is VERY SMART,really handsome,and INCREDIBLY FUNNY!!!",# booster words & punctuation make this close to ceiling for score
u"The book was good.",# positive sentence
u"The book was kind of good.",# qualified positive sentence is handled correctly (intensity adjusted)
u"The plot was good,but the characters are uncompelling and the dialog is not great.",# mixed negation sentence
u"A really bad,horrible book.",# negative sentence with booster words
u"At least it isn't a horrible book.",# negated negative sentence with contraction
u":) and :D",# emoticons handled
u"",# an empty string is correctly handled
u"Today sux",# negative slang handled
u"Today sux!",# negative slang with punctuation emphasis handled
u"Today SUX!",# negative slang with capitalization emphasis
u"Today kinda sux! But I'll get by,lol" # mixed sentiment example with slang and constrastive conjunction "but"
]
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. VERY BAD movie. VERY BAD movie!"
from nltk import tokenize
lines_list = tokenize.sent_tokenize(paragraph)
sentences.extend(lines_list)
tricky_sentences = [
"Most automated sentiment analysis tools are shit.","VADER sentiment analysis is the shit.","Sentiment analysis has never been good.","Sentiment analysis with VADER has never been this good.","Warren Beatty has never been so entertaining.","I won't say that the movie is astounding and I wouldn't claim that the movie is too banal either.","I like to hate Michael Bay films,but I couldn't fault this one","It's one thing to watch an Uwe Boll film,but another thing entirely to pay for it","The movie was too good","This movie was actually neither that funny,nor super witty.","This movie doesn't care about cleverness,wit or any other kind of intelligent humor.","Those who find ugly meanings in beautiful things are corrupt without being charming.","There are slow and repetitive parts,BUT it has just enough spice to keep it interesting.","The script is not fantastic,but the acting is decent and the cinematography is EXCELLENT!","Roger Dodger is one of the most compelling variations on this theme.","Roger Dodger is one of the least compelling variations on this theme.","Roger Dodger is at least compelling as a variation on the theme.","they fall in love with the product","but then it breaks","usually around the time the 90 day warranty expires","the twin towers collapsed today","However,Mr. Carter solemnly argues,his client carried out the kidnapping under orders and in the ''least offensive way possible.''"
]
sentences.extend(tricky_sentences)
for sentence in sentences:
print sentence
ss = sentiment(sentence)
print "t" + str(ss)
print "nn Done!"
