使用Python和NLTK进行基本文本分类

Question

因此，我一直在与Python和NLTK一起创建基本的文档分类器，以将主题分配给特定的.txt文件。

我是Python的新手，所以我确定代码不是很好，但是我遇到了一个特殊的问题。 我更改了九个#id以自动访问文本文件，因为我试图手动添加它们。 但是，它们只包含文件名，而不是包含文件文本的九个#_feats列表。 #id包含文件名和文本。 我认为问题出在def_wordfeats（words），但我不确定这是什么。 任何帮助，将不胜感激

import os
from nltk import FreqDist
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import stopwords
stopset = list(set(stopwords.words('english')))


1ids = {}
2ids = {}
3ids = {}
4ids = {}
5ids = {}
6ids = {}
7ids = {}
8ids = {}
9ids = {}


path1 = "/Users/myname/Documents/Data Classifier Files/1/"

for name in os.listdir(path1):
    if name[-4:] == '.txt':
        f = open(path1 + "/" + name, "r")
        1ids[name] = f.read()
        f.close()

path2 = "/Users/myname/Documents/Data Classifier Files/2/"

for name in os.listdir(path2):
    if name[-4:] == '.txt':
        f = open(path2 + "/" + name, "r")
        2ids[name] = f.read()
        f.close()        

path3 = "/Users/myname/Documents/Data Classifier Files/3/"

for name in os.listdir(path3):
    if name[-4:] == '.txt':
        f = open(path3 + "/" + name, "r")
        3ids[name] = f.read()
        f.close()        

path4 = "/Users/myname/Documents/Data Classifier Files/4/"

for name in os.listdir(path4):
    if name[-4:] == '.txt':
        f = open(path4 + "/" + name, "r")
        4ids[name] = f.read()
        f.close()        

path5 = "/Users/myname/Documents/Data Classifier Files/5/"

for name in os.listdir(path5):
    if name[-4:] == '.txt':
        f = open(path5 + "/" + name, "r")
        5ids[name] = f.read()
        f.close()        

path6 = "/Users/myname/Documents/Data Classifier Files/6/"

for name in os.listdir(path6):
    if name[-4:] == '.txt':
        f = open(path6 + "/" + name, "r")
        6ids[name] = f.read()
        f.close()        

path7 = "/Users/myname/Documents/Data Classifier Files/7/"

for name in os.listdir(path7):
    if name[-4:] == '.txt':
        f = open(path7 + "/" + name, "r")
        7ids[name] = f.read()
        f.close()        

path8 = "/Users/myname/Documents/Data Classifier Files/8/"

for name in os.listdir(path8):
    if name[-4:] == '.txt':
        f = open(path8 + "/" + name, "r")
        8ids[name] = f.read()
        f.close() 

path9 = "/Users/myname/Documents/Data Classifier Files/9/"

for name in os.listdir(path9):
    if name[-4:] == '.txt':
        f = open(path9 + "/" + name, "r")
        9ids[name] = f.read()
        f.close()         

def word_feats(words):
    return dict([(word, True) for word in words.split() if word not in stopset])

1_feats = [(word_feats(f), '1') for f in 1ids ]
2_feats = [(word_feats(f), "2") for f in 2ids ]
3_feats = [(word_feats(f), '3') for f in 3ids ]
4_feats = [(word_feats(f), '4') for f in 4ids ]
5_feats = [(word_feats(f), '5') for f in 5ids ]
6_feats = [(word_feats(f), '6') for f in 6ids ]
7_feats = [(word_feats(f), '7') for f in 7ids ]
8_feats = [(word_feats(f), '8') for f in 8ids ]
9_feats = [(word_feats(f), '9') for f in 9ids ]

trainfeats = 1_feats + 2_feats + 3_feats + 4_feats + 5_feats + 6_feats + 7_feats + 8_feats + 9_feats
classifier = NaiveBayesClassifier.train(trainfeats)

print classifier.classify(word_feats(""))

Answer 1

这里不对劲：

1_feats = [(word_feats(f), '1') for f in 1ids ]
2_feats = [(word_feats(f), "2") for f in 2ids ]
3_feats = [(word_feats(f), '3') for f in 3ids ]
4_feats = [(word_feats(f), '4') for f in 4ids ]
5_feats = [(word_feats(f), '5') for f in 5ids ]
6_feats = [(word_feats(f), '6') for f in 6ids ]
7_feats = [(word_feats(f), '7') for f in 7ids ]
8_feats = [(word_feats(f), '8') for f in 8ids ]
9_feats = [(word_feats(f), '9') for f in 9ids ]

当像您一样遍历python中的字典时，您正在遍历键。 就您而言，您要遍历文件名而不是文件的实际内容。

快速解决：

1_feats = [(word_feats(1ids[f]), '1') for f in 1ids ]