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我正在尝试实现朴素贝叶斯算法来对新闻报纸标题进行情感分析。我为此目的使用 TextBlob，我发现很难删除诸如“a”、“the”、“in”等停用词。下面是我在 python 中的代码片段：

from textblob.classifiers import NaiveBayesClassifier
from textblob import TextBlob

test = [
("11 bonded labourers saved from shoe firm", "pos"),
("Scientists greet Abdul Kalam after the successful launch of Agni on May 22, 1989","pos"),
("Heavy Winter Snow Storm Lashes Out In Northeast US", "neg"),
("Apparent Strike On Gaza Tunnels Kills 2 Palestinians", "neg")
       ]

with open('input.json', 'r') as fp:
cl = NaiveBayesClassifier(fp, format="json")

print(cl.classify("Oil ends year with biggest gain since 2009"))  # "pos"
print(cl.classify("25 dead in Baghdad blasts"))  # "neg"
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2 回答 2

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您可以先加载 json，然后使用替换创建元组列表（文本，标签）。

示范：

假设 input.json 文件是这样的：

[
    {"text": "I love this sandwich.", "label": "pos"},
    {"text": "This is an amazing place!", "label": "pos"},
    {"text": "I do not like this restaurant", "label": "neg"}
]

然后你可以使用：

from textblob.classifiers import NaiveBayesClassifier
import json

train_list = []
with open('input.json', 'r') as fp:
    json_data = json.load(fp)
    for line in json_data:
        text = line['text']
        text = text.replace(" is ", " ") # you can remove multiple stop words
        label = line['label']
        train_list.append((text, label))
    cl = NaiveBayesClassifier(train_list)

from pprint import pprint
pprint(train_list)

输出：

[(u'I love this sandwich.', u'pos'),
 (u'This an amazing place!', u'pos'),
 (u'I do not like this restaurant', u'neg')]
于 2017-02-20T18:42:58.113 回答
0

以下是删除文本中停用词的代码。将所有停用词放在停用词文件中，然后读取单词并存储到 stop_words 变量中。


# This function reads a file and returns its contents as an array
def readFileandReturnAnArray(fileName, readMode, isLower):
    myArray=[]
    with open(fileName, readMode) as readHandle:
        for line in readHandle.readlines():
            lineRead = line
            if isLower:
                lineRead = lineRead.lower()
            myArray.append(lineRead.strip().lstrip())
    readHandle.close()
    return myArray

stop_words = readFileandReturnAnArray("stopwords","r",True)

def removeItemsInTweetContainedInAList(tweet_text,stop_words,splitBy):
    wordsArray = tweet_text.split(splitBy)
    StopWords = list(set(wordsArray).intersection(set(stop_words)))
    return_str=""
    for word in wordsArray:
        if word not in StopWords:
            return_str += word + splitBy
    return return_str.strip().lstrip()


# Call the above method
tweet_text = removeItemsInTweetContainedInAList(tweet_text.strip().lstrip(),stop_words, " ")
于 2019-03-15T08:26:04.243 回答