小编gan*_*a75的帖子

我正在使用NLTK测试情感分析模型.我需要在分类器结果中添加一个Confusion Matrix,如果可能的话还需要在Precision,Recall和F-Measure值中添加.到目前为止我只有准确性.Movie_reviews数据有pos和neg标签.然而,为了训练分类器,我使用的"特征集"具有与通常(句子,标签)结构不同的格式.在通过"featuresets"训练分类器后,我不确定是否可以使用sklearn中的confusion_matrix

import nltk
import random
from nltk.corpus import movie_reviews

documents = [(list(movie_reviews.words(fileid)), category)
             for category in movie_reviews.categories()
             for fileid in movie_reviews.fileids(category)]

random.shuffle(documents)

all_words = []

for w in movie_reviews.words():
    all_words.append(w.lower())

all_words = nltk.FreqDist(all_words)

word_features = list(all_words.keys())[:3000]

def find_features(document):
    words = set(document)
    features = {}
    for w in word_features:
        features[w] = (w in words)

    return features


featuresets = [(find_features(rev), category) for (rev, category) in documents]

training_set = featuresets[:1900]
testing_set =  featuresets[1900:]


classifier = nltk.NaiveBayesClassifier.train(training_set)


print("Naive Bayes Algo accuracy percent:", (nltk.classify.accuracy(classifier, …