Datasets:
File size: 2,185 Bytes
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from datetime import datetime
from random import shuffle
import pandas as pd
from bs4 import BeautifulSoup
def parse_review(html):
if html.startswith('"') and html.endswith('"'):
html = html[1:-1]
soup = BeautifulSoup(html, "lxml")
text = soup.get_text()
return text
def parse_rating(rating_str):
rating = int(rating_str) - 1
return rating
def parse_date(date_str):
dt = datetime.strptime(date_str, "%B %d, %Y")
iso_format_date = dt.strftime("%Y-%m-%d")
return iso_format_date
def read_file(file_path):
data = pd.read_csv(file_path, sep="\t")
rows = []
for _, row in data.iterrows():
obj = {
"id": row[0],
"drugName": row[1],
"condition": row[2],
"review": parse_review(row[3]),
"rating": parse_rating(row[4]),
"date": parse_date(row[5]),
"usefulCount": row[6],
}
rows.append(obj)
return rows
def remap2sentiment(row):
if row["rating"] <= 3:
row["rating"] = 0
else:
row["rating"] = 1
return row
def save(rows, file_path):
df = pd.DataFrame(rows)
df.to_csv(file_path, index=False)
def run():
train_rows = read_file("drugsComTrain_raw.tsv")
test_rows = read_file("drugsComTest_raw.tsv")
all_rows = train_rows + test_rows
save(train_rows, "train.csv")
save(test_rows, "test.csv")
save(all_rows, "complete.csv")
# Extract only ratings 1, 2, 3, and 4 (negative reviews), and also
# ratings 7, 8, 9, and 10 (positive reviews)
negative_rows = [remap2sentiment(row) for row in all_rows if row["rating"] <= 3]
positive_rows = [remap2sentiment(row) for row in all_rows if row["rating"] >= 6]
positive_negative_rows = negative_rows + positive_rows
shuffle(positive_negative_rows)
save(positive_negative_rows, "positive_negative.csv")
# Balance the dataset
min_size = min(len(negative_rows), len(positive_rows))
balanced_rows = negative_rows[:min_size] + positive_rows[:min_size]
shuffle(balanced_rows)
save(balanced_rows, "positive_negative_balanced.csv")
if __name__ == "__main__":
run()
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