This phase involves two critical steps: text preprocessing and sentence tokenization, which are essential for enhancing the text's structure and readability for computational processing.

Text Preprocessing

The goal of preprocessing is to standardize the text, making it more amenable to analysis. This involves:

• Replacing specific characters: We target dashes (--), newline characters (\n), and quotation marks (") and replace them with spaces. This step helps eliminate inconsistencies and irregularities in the text's formatting that could hinder our analysis.
• Stripping leading and trailing spaces: By employing the .strip() method, we ensure that any extraneous whitespace at the beginning or end of our text is removed.

Sentence Tokenization

With our text now cleaned, the next step is to break it down into manageable units for analysis—specifically, into individual sentences. This process is known as sentence tokenization.

• Downloading necessary models: Before tokenizing, we ensure that the required models and datasets are available by downloading them using nltk.download('punkt'). This is a prerequisite for the sentence tokenization process.
• Applying the sentence tokenizer: Utilizing sent_tokenize from the NLTK library, we split our preprocessed text into a list of sentences. This function intelligently divides the text based on sentence boundaries, transforming a continuous block of text into a structured sequence of sentences.