Create README.md

README.md

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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)