对字符串的 Twitter 情绪分析

Question

我编写了一个程序，它采用包含推文和标签的推特数据（0 表示中性情绪，1 表示负面情绪）并预测推文属于哪个类别。 该程序在训练集和测试集上运行良好。但是我在使用字符串应用预测函数时遇到问题。我不知道该怎么做。

我已尝试以在调用预测函数之前清理数据集的方式清理字符串，但返回的值形状错误。

import numpy as np
import pandas as pd
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
ps = PorterStemmer()
import re

#Loading dataset
dataset = pd.read_csv('tweet.csv')

#List to hold cleaned tweets
clean_tweet = []

#Cleaning tweets
for i in range(len(dataset)):
    tweet = re.sub('[^a-zA-Z]', ' ', dataset['tweet'][i])
    tweet = re.sub('@[\w]*',' ',dataset['tweet'][i])
    tweet = tweet.lower()
    tweet = tweet.split()
    tweet = [ps.stem(token) for token in tweet if not token in set(stopwords.words('english'))]
    tweet = ' '.join(tweet)
    clean_tweet.append(tweet)

from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features = 3000)
X = cv.fit_transform(clean_tweet)
X =  X.toarray()
y = dataset.iloc[:, 1].values

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y)

from sklearn.naive_bayes import GaussianNB
n_b = GaussianNB()
n_b.fit(X_train, y_train)
y_pred  = n_b.predict(X_test) 

some_tweet = "this is a mean tweet"  # How to apply predict function to this string

Answer 1

在您的新字符串上使用cv.transform([cleaned_new_tweet]) 将您的新推文转换为您现有的文档术语矩阵。这将以正确的形状返回推文。

Answer 2

tl;博士

.predict() 需要 list 或 strings。所以你需要将some_tweet 添加到list。例如。 new_tweet = ["this is a mean tweet"]

您的代码

您的代码中存在一些问题，我已尝试为您修复...

import numpy as np
import pandas as pd
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
ps = PorterStemmer()
import re

#Loading dataset
dataset = pd.read_csv('tweet.csv')


# Define cleaning function
# You can define it once as a function so it can be easily re-used else where
def clean_tweet(tweet: str):
    tweet = re.sub('[^a-zA-Z]', ' ', dataset['tweet'][i])
    tweet = re.sub('@[\w]*', ' ', tweet) #BUG: you need to pass the tweet you modified here instead of the original tweet again
    tweet = tweet.lower()
    tweet = tweet.split()
    tweet = [ps.stem(token) for token in tweet if not token in set(stopwords.words('english'))]
    tweet = ' '.join(tweet)
    return tweet

#List to hold cleaned tweets and labels
X = [clean_tweet(tweet) for tweet in dataset['tweet']] # you can create your X directly with your new function
y = dataset.iloc[:, 1].values

# Define a single model
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import GaussianNB
from sklearn.pipeline import Pipeline

# Use Pipeline as your classifier, this way you don't need to keep calling a transform and fit all the time.
classifier = Pipeline(
    [
        ('cv', CountVectorizer(max_features=300)),
        ('n_b', GaussianNB())
    ]
)


# Before you trained your CountVectorizer BEFORE splitting into train/test. That is a biiig mistake.
# First you split to train/split and then you train all the steps of your model.

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y)

# Here you train all steps of your Pipeline in one go.
classifier.fit(X_train, y_train)
y_pred  = classifier.predict(X_test)


# Predicting new tweets
some_tweet = "this is a mean tweet"
some_tweet = clean_tweet(some_tweet) # re-use your clean function
predicted = classifier.predict([some_tweet]) # put the tweet inside a list!!!!