Python中文网 - 问答频道, 解决您学习工作中的Python难题和Bug
Python常见问题
我一直在使用维德情绪做一些文本情绪分析，我注意到我的数据中有很多“要走的路”短语，这些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}
在深入研究维德的源代码之后，我发现了以下字典：
^{pr2}$如您所见，我手动添加了“WayToGo”条目。然而，这似乎没有效果：
In [12]: sentiment('way to go John')
Out[12]: {'compound': 0.0, 'neg': 0.0, 'neu': 1.0, 'pos': 0.0}
你知道我错过了什么吗？或者更具体地说，我需要做些什么才能使添加自定义习惯用法发挥作用？以下是维德情感源代码：
#######################################################################################################################
# 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:\\Users\\jamacwan\\Code\\Python\\Twitter API\\Sentiment Analysis\\vader_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, "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": 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], v,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], v,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, 3),
"pos" : round(pos, 3),
"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, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted)
u"VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted)
u"VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled
u"VADER is VERY SMART, handsome, 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 "\n\n Done!"
Tags： andoftheinforifiscount
热门问题
- 我想从用户inpu创建一个类的实例
- 我想从用户导入值，为此
- 我想从用户那里得到一个整数输入，然后让for循环遍历该数字，然后调用一个函数多次
- 我想从用户那里收到一个列表，并在其中执行一些步骤，然后在步骤完成后将其打印回来，但它没有按照我想要的方式工作
- 我想从用户那里获取输入，并将值传递给（average=dict[x]/6），然后在那里获取resu
- 我想从第一个列表中展示第一个词，然后从第二个列表中展示十个词，以此类推- Python
- 我想从第一个空lin开始解析文本文件
- 我想从简历、简历中提取特定部分
- 我想从给定字典（python）的字符串中删除\u00a9、\u201d和类似的字符。
- 我想从给定的网站Lin下载许多文件扩展名相同的Wget或Python文件
- 我想从网上搜集一些关于抵押贷款的数据
- 我想从网站上删除电子邮件地址
- 我想从网站上读取数据该网站包含可下载的文件，然后我想用python脚本把它发送给oracle如何？
- 我想从网站中提取数据，然后将其显示在我的网页上
- 我想从网页上提取统计数据。
- 我想从网页上解析首都城市，并在用户输入国家时在终端上打印它们
- 我想从色彩图中删除前n个颜色，而不丢失原始颜色数
- 我想从课堂上打印字典里的键
- 我想从费用表中获取学生上次支付的费用，其中学生id=id
- 我想从较低的顺序对多重列表进行排序，但我无法在一行中生成结果
热门文章
- Python覆盖写入文件
- 怎样创建一个 Python 列表？
- Python3 List append()方法使用
- 派森语言
- Python List pop()方法
- Python Django Web典型模块开发实战
- Python input() 函数
- Python3 列表(list) clear()方法
- Python游戏编程入门
- 如何创建一个空的set?
- python如何定义(创建)一个字符串
- Python标准库 [The Python Standard Library by Ex
- Python网络数据爬取及分析从入门到精通（分析篇）
- Python3 for 循环语句
- Python List insert() 方法
- Python 字典(Dictionary) update()方法
- Python编程无师自通 专业程序员的养成
- Python3 List count()方法
- Python 网络爬虫实战 [Web Crawler With Python]
- Python Cookbook（第2版）中文版
该代码有几个问题：
特殊情况仅适用于
vader_sentiment_lexicon.txt中的单词，因为：如果您将短语更改为包含这样一个词，例如“放弃”，则此项通过ok。 如何修复：
^{pr2}$再加上一些内部修复。
只有在特殊单词至少有第三个位置（索引>2）时，才会检查特殊情况。在
这里的短语
way to abandon John单词abandon有索引2，但没有这样的情况。如果我们将短语改为you way to abandon John，那么它就开始工作了。 解决方法：将特殊情况向上移动一个分支。或者最好使用实际长度的特殊情况，而不是尝试硬编码。简历：代码在支持上并不容易。在
相关问题 更多 >
编程相关推荐