Question

我正在尝试对评论进行情感分析；数据集包含两个主要的列：第一个是具有用户评论的“评论”，第二个列是正面还是负面。我从源头获得了处理数据的模板，培训和测试还可以。但是，我想输入一个文本并希望模型预测它是正数还是负数。我尝试了很多形式的输入：仅字符串，字符串列表，从numpy到数组等。有什么想法如何输入要预测的数据？这是我的代码：

import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter='\t',quoting=3)

import re 
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
corpus=[]
for i in range(0,1000):
    review=re.sub('[^a-zA-Z]',' ',dataset['Review'][i])
    review.lower()
    review=review.split()
    ps=PorterStemmer()
    review=[ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
    review=' '.join(review)
    corpus.append(review)

#the bag of word
from sklearn.feature_extraction.text import CountVectorizer
cv=CountVectorizer(max_features=1500)
X=cv.fit_transform(corpus).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, test_size = 0.2, random_state = 0)



# Fitting Naive Bayes to the Training set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)

# Predicting the Test set results
xeval=["I like it okay"]
prediction=classifier.predict(xeval)```

the error in this case is:
Expected 2D array, got 1D array instead:
array=['I like it okay'].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Answer 1

正如安德森（G.Anderson）所提到的那样，您的分类器是使用数字数据训练的，就像您以前使用的那样：

X=cv.fit_transform(corpus).toarray()

为此而制作了CountVectorizer。

要使用它，还必须使用训练有素的CountVectorizer，您必须实现：

# Predicting the Test set results
xeval=["I like it okay"]
xeval_numeric = cv.transform(xeval).toarray() 
prediction=classifier.predict(xeval_numeric)

在NLP中训练数据集后如何预测标签

1 个答案: