Question

我试图将Naive Bayes算法用于新闻报纸标题的情绪分析。我为此目的使用TextBlob，我发现很难删除诸如＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39;＆＃39; ;以下是我在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"

Answer 1

您可以先加载json，然后使用替换创建元组列表（text，label）。

演示：

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')]

Answer 2

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，然后读取单词并将其存储到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, " ")

在文本情感分析中删除textblob中的停用词的有效方法是什么？

2 个答案: