我指的是http://www.nltk.org/book/ch06.html用于生成电影评论分类器。它将所有单词（名词、形容词、动词……）视为特征集的一部分。我正在尝试构建一个仅考虑动词并评估电影评论是正面还是负面的分类器。

请解释这种方法是否更好，如果是，如何改进，否则需要包含哪些其他词性标签来改进功能集。
请参考以下代码
1）导入 nltk 和语料库
2）了解类别
3）创建“文档”列表，其中每个文档由不在 stopwords.words() 中的单词组成，并从“标点符号”列表中过滤为好。
4) 打乱文档并生成一个名为“all_words”的列表，其中包含movie_reviews 中出现的所有单词减去停用词和标点符号。
5）创建“all_words”的频率分布
6）通过查看出现在“all_words”中的每个单词的 pos_tag 创建“动词”列表
7) 为步骤 3 中创建的“文档”列表中的每个文档创建一个特征字典，这样字典将包含与“动词”列表中的单词对应的键和一个布尔值，以显示该动词是否出现在所考虑的文档中。这由 documentFeature() 处理。
8）创建一个朴素贝叶斯分类器实例并训练和计算测试准确度。

from nltk.corpus import movie_reviews
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
import nltk
movie_reviews.categories()
#['pos','neg']
#the regextokenier is used to tokenize the words    
tokenizer=RegexpTokenizer(r'\w+')
#creating documents based on filtration of stopwords for each review and running a tokenizer on each document
documents=[(tokenizer.tokenize(' '.join(set(i for i in movie_reviews.words(fileid))-set(stopwords.words()))),category) 
           for category in movie_reviews.categories()
           for fileid in movie_reviews.fileids() 
          ]
import random
random.shuffle(documents)
#each document contains words that are not in stopwords and punctuations.
for i in documents[:5]:
    temp=nltk.FreqDist([j.lower() for j in i[0]])
    print(temp.most_common(5),i[1]) 
#output of 5 documents
#[('vampires', 1), ('clever', 1), ('interesting', 1), ('sunlight', 1), ('partners', 1)] neg
#[('family', 1), ('nino', 1), ('friends', 1), ('acting', 1), ('higher', 1)] pos
#[('inconsistent', 1), ('eye', 1), ('yes', 1), ('interesting', 1), ('praise', 1)] neg
#[('acting', 1), ('science', 1), ('bucks', 1), ('huge', 1), ('terrific', 1)] pos
#[('acting', 1), ('shielded', 1), ('somewhere', 1), ('think', 1), ('touched', 1)] neg

#generate a list called 'all_words' that contains all the set of words that have appeared so far
all_words=tokenizer.tokenize(' '.join(set(i for i in movie_reviews.words())-set(stopwords.words())))
freqdist=nltk.FreqDist(all_words)

#create a list of all verbs for each word appearing in 'all_words'
verb=[]
pos_=nltk.pos_tag(all_words)
#print([i[1] for i in pos_])
for i in pos_:
    if i[1] in ['VB','VBG','VBN','VBZ','VBD','VBP']:
        verb.append(i[0])

#document - feature set, build a dictionary of verbs for each document
def documentFeature(document):
    feature={}
    for i in verb:
        feature['contains({0})'.format(i)]=(i in document)
    return feature    
#build a naive bayes classifier
featureSet=[(documentFeature(d),c) for d,c in documents]
trainSet,testSet=featureSet[100:], featureSet[:100]
classifier=nltk.NaiveBayesClassifier.train(trainSet)

print(nltk.classify.accuracy(classifier, testSet))
#0.03 a very poor accuracy on the testset

目前我的准确度为 0.03，请帮助我提高准确度。