Python NLTK 和 Pandas - 文本分类器 - （新手） - 以类似于提供的示例的格式导入我的数据

Question

我是文本分类的新手，但我了解大部分概念。简而言之，我在 Excel 数据集中有一个餐厅评论列表，我想将它们用作我的训练数据。我挣扎的地方是将实际评论和分类（1 = pos，0 = neg）作为我的训练数据集的一部分导入的示例语法。如果我在一个元组中手动创建我的数据集（即，我当前的数据集在训练中已经#'ed out），我知道如何做到这一点。任何帮助表示赞赏。

import nltk
from nltk.tokenize import word_tokenize
import pandas as pd

df = pd.read_excel("reviewclasses.xlsx")

customerreview= df.customerreview.tolist() #I want this to be what's in 
"train" below (i.e., "this is a negative review")

reviewrating= df.reviewrating.tolist() #I also want this to be what's in 
"train" below (e.g., 0)

#train = [("Great place to be when you are in Bangalore.", "1"),
#  ("The place was being renovated when I visited so the seating was 
limited.", "0"),
#  ("Loved the ambiance, loved the food", "1"),
#  ("The food is delicious but not over the top.", "0"),
#  ("Service - Little slow, probably because too many people.", "0"),
#  ("The place is not easy to locate", "0"),
#  ("Mushroom fried rice was spicy", "1"),
#]

dictionary = set(word.lower() for passage in train for word in 
word_tokenize(passage[0]))

t = [({word: (word in word_tokenize(x[0])) for word in dictionary}, x[1]) 
for x in train]

# Step 4 – the classifier is trained with sample data
classifier = nltk.NaiveBayesClassifier.train(t)

test_data = "The food sucked and I couldn't wait to leave the terrible 
restaurant."
test_data_features = {word.lower(): (word in 
word_tokenize(test_data.lower())) for word in dictionary}

print (classifier.classify(test_data_features))

Answer 1

我明白了。我基本上只需要将两个列表组合成一个元组。

def merge(customerreview, reviewrating): 

    merged_list = [(customerreview[i], reviewrating[i]) for i in range(0, 
len(customerreview))] 
    return merged_list 

train = (merge(customerreview, reviewrating))

Python NLTK 和 Pandas - 文本分类器 - （新手） - 以类似于提供的示例的格式导入我的数据

Python NLTK and Pandas - text classifier - (newbie ) - importing my data in a format similar to provided example

python

nlp

nltk

pandas

text-classification