python удалить спам с пунктуацией в электронной почте

Question

Попытка убрать знаки препинания из списка слов. Новичок в программировании python, так что, если бы кто-то мог помочь, это было бы здорово. Это используется для классификации спама в электронной почте. Раньше я присоединял слова после проверки, присутствует ли пунктуация, но это давало мне отдельные символы, а не целые слова. После изменения, чтобы получить слова, это то, что у меня есть ниже, поэтому теперь пытаюсь удалить пунктуацию, так как не будет работать так же, как я раньше.

import os
import string
from collections import Counter
from os import listdir  # return all files and folders in the directory

import nltk
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
from nltk import WordNetLemmatizer
from nltk.corpus import stopwords
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer

# used for importing the lingspam dataset
def importLingspamDataset(dir):
    allEmails = [] # for storing the emails once read
    fileNames = []
    for file in listdir(dir):
        f = open((dir + '/' + file), "r")  # used for opening the file in read only format
        fileNames.append(file)
        allEmails.append(f.read()) # appends the read emails to the emails array
        f.close()
    return allEmails, fileNames

def importEnronDataset(dir):
    allEmails = []  # for storing the emails once read
    fileNames = []
    for file in listdir(dir):
        f = open((dir + '/' + file), "r")  # used for opening the file in read only format
        fileNames.append(file)
        allEmails.append(f.read())  # appends the read emails to the emails array
        f.close()
        return allEmails, fileNames

# used to remove punctuation from the emails as this is of no use for detecting spam
def removePunctuation(cleanedEmails):
    punc = set(string.punctuation)
    for word, line in enumerate(cleanedEmails):
        words = line.split()
        x = [''.join(c for c in words if c not in string.punctuation)]
        allWords = []
        allWords += x
        return allWords

# used to remove stopwords i.e. words of no use in detecting spam
def removeStopwords(cleanedEmails):
    removeWords = set(stopwords.words('english')) # sets all the stopwords to be removed
    for stopw in removeWords: # for each word in remove words
        if stopw not in removeWords: # if the word is not in the stopwords to be removed
            cleanedEmails.append(stopw) # add this word to the cleaned emails
    return(cleanedEmails)

# funtion to return words to its root form - allows simplicity
def lemmatizeEmails(cleanedEmails):
    lemma = WordNetLemmatizer() # to be used for returning each word to its root form
    lemmaEmails = [lemma.lemmatize(i) for i in cleanedEmails] # lemmatize each word in the cleaned emails
    return lemmaEmails

# function to allow a systematic process of elimating the undesired elements within the emails
def cleanAllEmails(cleanedEmails):
    cleanPunc = removePunctuation(cleanedEmails)
    cleanStop = removeStopwords(cleanPunc)
    cleanLemma = lemmatizeEmails(cleanStop)
    return cleanLemma

def createDictionary(email):
    allWords = []
    allWords.extend(email)
    dictionary = Counter(allWords)
    dictionary.most_common(3000)
    word_cloud = WordCloud(width=400, height=400, background_color='white',
              min_font_size=12).generate_from_frequencies(dictionary)
    plt.imshow(word_cloud)
    plt.axis("off")
    plt.margins(x=0, y=0)
    plt.show()
    word_cloud.to_file('test1.png')

def featureExtraction(email):
     emailFiles = []
     emailFiles.extend(email)
     featureMatrix = np.zeros((len(emailFiles), 3000))


def classifyLingspamDataset(email):
    classifications = []
    for name in email:
         classifications.append("spmsg" in name)
    return classifications

# Lingspam dataset
trainingDataLingspam, trainingLingspamFilename = importLingspamDataset("spam-non-spam-dataset/train-mails") # extract the training emails from the dataset
#testingDataLingspam, testingLingspamFilename = importLingspamDataset("spam-non-spam-dataset/test-mails") # extract the testing emails from the dataset

trainingDataLingspamClean = cleanAllEmails(trainingDataLingspam)
#testingDataLingspamClean = cleanAllEmails(testingDataLingspam)

#trainClassifyLingspam = classifyLingspamDataset(trainingDataLingspam)
#testClassifyLingspam = classifyLingspamDataset(testingDataLingspam)

trainDictionary = createDictionary(trainingDataLingspamClean)
#createDictionary(testingDataLingspamClean)

#trainingDataEnron, trainingEnronFilename = importEnronDataset("spam-non-spam-dataset-enron/bigEmailDump/training/")

Answer 1

Исходя из вашего вопроса, я предполагаю, что у вас есть список электронных писем, в каждом из которых вы хотите удалить знаки препинания. Этот ответ основан на первой версии опубликованного вами кода.

import string


def removePunctuation(emails):

    # I am using a list comprehension here to iterate over the emails.
    # For each iteration, translate the email to remove the punctuation marks.
    # Translate only allows a translation table as an argument.
    # This is why str.maketrans is used to create the translation table.

    cleaned_emails = [email.translate(str.maketrans('', '', string.punctuation))
                      for email in emails]

    return cleaned_emails


if __name__ == '__main__':

    # Assuming cleanedEmails is a list of emails, 
    # I am substituting cleanedEmails with emails.
    # I used cleanedEmails as the result.

    emails = ["This is a, test!", "This is another#@! \ntest"]
    cleaned_emails = removePunctuation(emails)
    print(cleaned_emails)

input: ["This is a, test!", "This is another#@! \ntest"]
output: ['This is a test', 'This is another \ntest']

EDIT:

Проблема решена после разговора с OP. У OP возникла проблема с WordCloud, и предложенное мной решение работает. Успейте направить OP через работу WordCloud. OP теперь выполняет точную настройку результатов WordCloud.