| 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) |