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The text file contains the collection of short movie reviews.
Each review is enclosed in parentheses and consists of a numerical rating followed by the review text.
The numerical rating is on a scale of 0 to 4, where higher numbers indicate a more positive review.
Here are some additional observations:
Format: The reviews follow a consistent format with the rating at the beginning, making it easy to identify the sentiment of each review.
Concise: The reviews are generally concise, focusing on key aspects of the movie.
Variety of Movies: The reviews cover a wide range of movies, from dramas and comedies to documentaries and action films.
Diverse Opinions: The ratings suggest a diversity of opinions on the movies.
The text file contains sentences that are hierarchically structured, likely using nested parentheses to represent some form of syntactic parsing.
To convert this into a format suitable for sentiment analysis, we need to extract the sentences in a plain text format and load them into a DataFrame.
Follow the below code for the above process
import pandas as pd
import re
labels = []
texts = []
with open("train.txt", "r") as file:
for line in file:
match = re.search(r'\((\d+) \((.*)\)\)', line)
if match:
labels.append(int(match.group(1)))
texts.append(match.group(2))
df = pd.DataFrame({"label": labels, "text": texts})
df.to_csv("output.csv", index=False)
import pandas as pd
import re
df = pd.read_csv("output.csv")
pattern = r'[().,;:"!?0-9]'
df['cleaned_text'] = df['text'].astype(str).apply(lambda x: re.sub(pattern, '', x))
df[['label', 'cleaned_text']].to_csv("cleaned_output.csv", index=False)
df = df.drop('text' , axis=1)
df.to_csv('sentence_train.csv' , index=False) |