import pandas as pd
from bs4 import BeautifulSoup

with open("sorted_data/books/positive.review") as file:
    soup_pos = BeautifulSoup(file, "lxml")

texts_pos=[]
for review in soup_pos.find_all("review"):
    texts_pos.append(review.review_text.text)

books_pos = pd.DataFrame({"text":texts_pos, "label":1})
books_pos.text = books_pos.text.replace("^\n", "", regex=True)

from sklearn.model_selection import train_test_split
books_pos_train, books_pos_test = train_test_split(books_pos, train_size=800, random_state=41)

# Not OK to sart with, OK after open+save in Sublime
# (encoding situation)
with open("sorted_data/books/negative.review") as file:
    soup_neg = BeautifulSoup(file, "lxml")

texts_neg=[]
for review in soup_neg.find_all("review"):
    texts_neg.append(review.review_text.text)

books_neg = pd.DataFrame({"text":texts_neg, "label":0})
books_neg.text = books_neg.text.replace("^\n", "", regex=True)

books_neg_train, books_neg_test = train_test_split(books_neg, train_size=800)

pd.concat([books_pos_train, books_neg_train]).to_csv("sorted_data/books/train.csv", index=False)
pd.concat([books_pos_test,  books_neg_test ]).to_csv("sorted_data/books/test.csv", index=False)