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import pandas as pd |
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from bs4 import BeautifulSoup |
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with open("sorted_data/books/positive.review") as file: |
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soup_pos = BeautifulSoup(file, "lxml") |
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texts_pos=[] |
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for review in soup_pos.find_all("review"): |
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texts_pos.append(review.review_text.text) |
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books_pos = pd.DataFrame({"text":texts_pos, "label":1}) |
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books_pos.text = books_pos.text.replace("^\n", "", regex=True) |
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from sklearn.model_selection import train_test_split |
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books_pos_train, books_pos_test = train_test_split(books_pos, train_size=800, random_state=41) |
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with open("sorted_data/books/negative.review") as file: |
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soup_neg = BeautifulSoup(file, "lxml") |
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texts_neg=[] |
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for review in soup_neg.find_all("review"): |
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texts_neg.append(review.review_text.text) |
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books_neg = pd.DataFrame({"text":texts_neg, "label":0}) |
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books_neg.text = books_neg.text.replace("^\n", "", regex=True) |
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books_neg_train, books_neg_test = train_test_split(books_neg, train_size=800) |
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pd.concat([books_pos_train, books_neg_train]).to_csv("sorted_data/books/train.csv", index=False) |
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pd.concat([books_pos_test, books_neg_test ]).to_csv("sorted_data/books/test.csv", index=False) |
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