Create preprocessing.py

tasks/utils/preprocessing.py

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+import nltk
+from nltk.corpus import stopwords
+import spacy
+
+nltk.download('stopwords')
+# Get the list of English stop words from NLTK
+nltk_stop_words = stopwords.words('english')
+# Load the spaCy model for English
+nlp = spacy.load("en_core_web_sm")
+
+
+def process_text(text):
+    """
+    Process text by:
+    1. Lowercasing
+    2. Removing punctuation and non-alphanumeric characters
+    3. Removing stop words
+    4. Lemmatization
+    """
+    # Step 1: Tokenization & Processing with spaCy
+    doc = nlp(text.lower())  # Process text with spaCy
+
+    # Step 2: Filter out stop words, non-alphanumeric characters, punctuation, and apply lemmatization
+    processed_tokens = [
+        re.sub(r'[^a-zA-Z0-9]', '', token.lemma_)  # Remove non-alphanumeric characters
+        for token in doc 
+        if token.text not in nltk_stop_words and token.text not in string.punctuation
+    ]
+    
+    # Optional: Filter out empty strings resulting from the regex replacement
+    processed_tokens = " ".join([word for word in processed_tokens if word])
+    
+    return processed_tokens