我正在使用nltk的朴素贝叶斯分类器对某些推文执行情感分析。我正在使用https://towardsdatascience.com/creating-the-twitter-sentiment-analysis-program-in-python-with-naive-bayes-classification-672e5589a7ed处的语料库文件以及此处的方法来训练数据。
在创建训练集时，我已经使用数据集中的所有〜4000条推文完成了训练，但我还认为我会用很少的30条推子进行测试。
在对整个集合进行测试时，当在新的一组推文上使用分类器时，它仅返回“中性”作为标签，但是在使用30条时，它只会返回正数，这是否意味着我的训练数据不完整或过于繁重使用中性条目进行“加权”，并且为什么我的分类器仅在训练集中使用约4000条推文时才返回中性？
我在下面提供了完整的代码。
twitter_api = twitter.Api(consumer_key = consumer_key,
consumer_secret = consumer_secret,
access_token_key = access_token,
access_token_secret = access_token_secret)
# Test set builder
def buildtestset(keyword):
try:
min_id = None
tweets = []
ids = []
for i in range(0,50):
tweetsdata = twitter_api.GetSearch(keyword, count = 100, max_id = min_id )
for t in tweetsdata:
tweets.append(t)
ids.append(t.id)
min_id = min(ids)
print(str(len(tweets))+ ' tweets found for keyword: '+keyword)
return[{"text":status.text, "label":None} for status in tweets]
except:
print('this is so sad')
return None
# Quick test
keyword = 'bicycle'
testdataset = buildtestset(keyword)
# Training set builder
def buildtrainingset(corpusfile,tweetdata):
#corpusfile = pathway to corpus data
#tweetdata = pathway to file we going to save all the tweets to
corpus = []
with open(corpusfile,'r') as csvfile:
linereader = csv.reader(csvfile, delimiter = ',', quotechar = "\"")
for row in linereader:
corpus.append({'tweet_id':row[2],'label':row[1],'topic':row[0]})
# Append every tweet from corpusfile to our corpus list
rate_limit = 180
sleep_time = 900/180
# these are set up so we call enough times to be within twitters guidelines
# the rest is calling the api of every tweet to get the status object, text associated with it and then put it in our
# data set - trainingdata
trainingdata = []
count = 0
for tweet in corpus:
if count < 30:
try:
status = twitter_api.GetStatus(tweet['tweet_id'])
print ('Tweet fetched '+status.text)
tweet['text'] = status.text
trainingdata.append(tweet)
time.sleep(sleep_time)
count += 1
except:
count += 1
continue
#write tweets to empty csv
with open(tweetdata,'w',encoding='utf-8') as csvfile:
linewriter = csv.writer(csvfile, delimiter=',',quotechar = "\"")
for tweet in trainingdata:
try:
linewriter.writerow([tweet['tweet_id'],tweet['text'],tweet['label'],tweet['topic']])
except Exception as e:
print(e)
return trainingdata
corpusfile = (r'C:\Users\zacda\OneDrive\Desktop\DATA2901\Assignment\corpusmaster.csv')
tweetdata = (r'C:\Users\zacda\OneDrive\Desktop\DATA2901\Assignment\tweetdata.csv')
TrainingData = buildtrainingset(corpusfile,tweetdata)
import re # regular expression library
from nltk.tokenize import word_tokenize
from string import punctuation
from nltk.corpus import stopwords
class preprocesstweets:
def __init__(self):
self._stopwords = set(stopwords.words('english') + list(punctuation) + ['AT_USER','URL'])
def processtweets(self, list_of_tweets):
processedtweets=[]
for tweet in list_of_tweets:
processedtweets.append((self._processtweet(tweet["text"]),tweet["label"]))
return processedtweets
def _processtweet(self, tweet):
tweet = tweet.lower() # convert text to lower-case
tweet = re.sub('((www\.[^\s]+)|(https?://[^\s]+))', 'URL', tweet) # remove URLs
tweet = re.sub('@[^\s]+', 'AT_USER', tweet) # remove usernames
tweet = re.sub(r'#([^\s]+)', r'\1', tweet) # remove the # in #hashtag
tweet = word_tokenize(tweet) # remove repeated characters (helloooooooo into hello)
return [word for word in tweet if word not in self._stopwords]
tweetprocessor = preprocesstweets()
processedtrainingdata = tweetprocessor.processtweets(TrainingData)
processedtestdata = tweetprocessor.processtweets(testdataset)
# This is a list of all the words we have in the training set, the word_features is a list of all the distinct words w freq
import nltk
def buildvocab(processedtrainingdata):
all_words = []
for (words, sentiment) in processedtrainingdata:
all_words.extend(words)
wordlist = nltk.FreqDist(all_words)
word_features = wordlist.keys()
return word_features
def extract_features(tweet):
tweet_words = set(tweet)
features = {}
for word in word_features:
features['contains(%s)' % word] = (word in tweet_words) #creates json key containing word x, its loc.
# Every key has a T/F according - true for present , false for not
return features
# Building the feature vector
word_features = buildvocab(processedtrainingdata)
training_features = nltk.classify.apply_features(extract_features, processedtrainingdata)
# apply features does the actual extraction
# Naive Bayes Classifier
Nbayes = nltk.NaiveBayesClassifier.train(training_features)
Nbayes_result_labels = [Nbayes.classify(extract_features(tweet[0])) for tweet in processedtestdata]
# get the majority vote [?]
if Nbayes_result_labels.count('positive') > Nbayes_result_labels.count('negative'):
print('Positive')
print(str(100*Nbayes_result_labels.count('positive')/len(Nbayes_result_labels)))
elif Nbayes_result_labels.count('negative') > Nbayes_result_labels.count('positive'):
print(str(100*Nbayes_result_labels.count('negative')/len(Nbayes_result_labels)))
print('Negative sentiment')
else:
print('Neutral')
答案 0 :(得分：0)
进行机器学习时，我们想学习一种在新（看不见的）数据上表现良好的算法。这称为概括。
除其他事项外，测试集的目的是验证分类器的泛化行为。如果您的模型为每个测试实例预测相同的标签，则我们无法确认该假设。测试集应该代表您以后使用的条件。
根据经验，我想认为您保留其数据的50％到25％作为测试集。这当然取决于情况。 30/4000小于百分之一。
想到的第二点是，当您的分类器偏向一个班级时，请确保每个班级在培训和验证集中的代表几乎相等。这样可以防止分类器“仅仅”学习整个集合的分布，而不是学习哪些特征相关。
最后一点，通常我们报告精度，召回率和F β= 1 等指标来评估我们的分类器。样本中的代码似乎基于所有推文中的全球情绪来报告某些内容，您确定这就是您想要的吗？这些推文是否具有代表性？