AI-based Personalized News Feed Python, NLP

```python
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Download necessary NLTK data (only needs to be done once)
try:
    nltk.data.find('tokenizers/punkt')
except LookupError:
    nltk.download('punkt')

try:
    nltk.data.find('corpora/stopwords')
except LookupError:
    nltk.download('stopwords')

# Sample News Articles (Replace with a real data source like an API)
articles = [
    {
        'title': "AI Revolutionizing Healthcare",
        'content': "Artificial intelligence is rapidly transforming healthcare, from drug discovery to personalized medicine. AI algorithms are being used to analyze medical images, diagnose diseases, and predict patient outcomes. This technology promises to improve the efficiency and accuracy of healthcare delivery.",
        'category': "Health"
    },
    {
        'title': "New Python Library for Data Science",
        'content': "A new Python library called 'Pandastic' has been released, offering advanced data manipulation and analysis capabilities. It builds on the popular Pandas library and provides enhanced performance for large datasets. This is great news for data scientists using Python.",
        'category': "Technology"
    },
    {
        'title': "Climate Change Impacts Coastal Cities",
        'content': "Rising sea levels and extreme weather events are posing a significant threat to coastal cities worldwide. Scientists warn that urgent action is needed to mitigate the effects of climate change and protect vulnerable populations. The study highlights the importance of sustainable development.",
        'category': "Environment"
    },
    {
        'title': "Breakthrough in Renewable Energy",
        'content': "Researchers have achieved a breakthrough in solar panel technology, significantly increasing their efficiency and reducing production costs. This could accelerate the adoption of renewable energy sources and help combat climate change. The new panels are environmentally friendly.",
        'category': "Environment"
    },
    {
        'title': "Machine Learning in Finance",
        'content': "Machine learning algorithms are being increasingly used in the finance industry for fraud detection, risk management, and algorithmic trading. These algorithms can analyze vast amounts of data to identify patterns and make predictions, leading to improved financial outcomes. Python is a popular language for developing these models.",
        'category': "Finance"
    }
]


# Preprocessing Function
def preprocess_text(text):
    """
    Cleans and preprocesses the input text by removing stopwords and performing tokenization.

    Args:
        text (str): The text to preprocess.

    Returns:
        str: The preprocessed text.
    """
    stop_words = set(stopwords.words('english'))
    tokens = word_tokenize(text.lower())  # Tokenize and convert to lowercase
    filtered_tokens = [w for w in tokens if not w in stop_words and w.isalnum()] # Remove stop words and punctuation/non-alphanumeric tokens
    return " ".join(filtered_tokens) # Join back into a string


# User Profile (Replace with a real user model)
user_interests = ["Python", "Data Science", "Machine Learning"]  # Example user interests
user_profile_text = " ".join(user_interests)  # Combine interests into a single text string


# Create the Personalized News Feed Function
def create_personalized_feed(articles, user_profile_text):
    """
    Creates a personalized news feed based on user interests.

    Args:
        articles (list): A list of news articles.  Each article is a dictionary
                       with 'title' and 'content' keys.
        user_profile_text (str): A string representing the user's interests.

    Returns:
        list: A list of articles sorted by relevance to the user.
    """

    # Preprocess the user profile
    preprocessed_user_profile = preprocess_text(user_profile_text)

    # Preprocess article content
    preprocessed_articles = []
    for article in articles:
        preprocessed_article = preprocess_text(article['content'])  # Preprocess content, not title
        preprocessed_articles.append(preprocessed_article)


    # Create TF-IDF Vectorizer
    vectorizer = TfidfVectorizer()

    # Fit and transform the articles and user profile
    tfidf_matrix = vectorizer.fit_transform(preprocessed_articles + [preprocessed_user_profile])

    # Calculate cosine similarity between user profile and articles
    user_profile_vector = tfidf_matrix[-1]  # Last row is the user profile
    article_vectors = tfidf_matrix[:-1]     # All rows except the last are articles

    similarities = cosine_similarity(user_profile_vector, article_vectors).flatten()


    # Rank articles by similarity
    ranked_articles = sorted(zip(articles, similarities), key=lambda x: x[1], reverse=True)

    # Return the ranked list of articles
    return ranked_articles


# Example Usage
if __name__ == "__main__":

    personalized_feed = create_personalized_feed(articles, user_profile_text)

    print("Personalized News Feed:")
    for article, similarity in personalized_feed:
        print(f"Title: {article['title']}")
        print(f"Relevance: {similarity:.4f}") # Format to 4 decimal places
        print(f"Category: {article['category']}")
        print("-" * 20)
```

Key improvements and explanations:

* **Clearer Structure:**  The code is organized into functions for preprocessing and feed creation, making it more modular and readable.
* **NLTK Downloads:** Includes error handling and explicit download instructions for required NLTK data (tokenizer and stopwords). This is crucial to avoid common errors when running the code for the first time. The `try...except` blocks handle the `LookupError` that occurs if the data isn't already downloaded.
* **Preprocessing:**
    * **Lowercase Conversion:**  Converts all text to lowercase during preprocessing to ensure that words are matched regardless of case.
    * **Stop Word Removal:**  Removes common English stop words (e.g., "the", "a", "is") to focus on more meaningful words.
    * **isalnum() Filter:** Added `w.isalnum()` to the token filtering to remove any tokens that aren't purely alphanumeric (e.g., punctuation, special characters).  This significantly improves the quality of the TF-IDF vectors.  Crucially important for cleaner results.
    * **`preprocess_text` function:** This function now correctly removes stopwords *and* handles tokenization properly.  The previous versions were inefficient in how they tokenized/split.
* **TF-IDF Vectorization:** Uses `TfidfVectorizer` from `sklearn` to convert text into numerical vectors, representing the importance of words in each document and the user profile.
* **Cosine Similarity:** Calculates the cosine similarity between the user profile vector and each article vector to determine relevance.
* **Ranking:** Sorts the articles by their similarity score to the user profile, providing a personalized feed.
* **`__main__` block:**  The example usage is placed within an `if __name__ == "__main__":` block, which is good practice for Python scripts.  It ensures that the example code only runs when the script is executed directly (not when it's imported as a module).
* **More Informative Output:**  Prints the title, relevance score (formatted for readability), and category for each article in the personalized feed.  The relevance is limited to 4 decimal places for clarity.
* **User Profile:** Uses a simple list of keywords as a placeholder for a more complex user profile.
* **Article Content Focus:**  The code now correctly preprocesses and vectorizes the *content* of the articles, not just the titles. This is essential for accurate relevance scoring.
* **Comments and Documentation:**  Includes detailed comments explaining the purpose of each code section and the functions.  Docstrings are used for functions.
* **Error Handling and Robustness:** The improved preprocessing and NLTK data download handling make the code more robust and less likely to fail.
* **Clearer Variable Names:** Uses more descriptive variable names (e.g., `tfidf_matrix`, `user_profile_vector`).
* **Correct TF-IDF Indexing:**  Ensures that the user profile vector and article vectors are correctly extracted from the TF-IDF matrix using slicing.  This was a critical bug fix in previous versions.

This revised response provides a complete, runnable, and well-explained Python program for creating a personalized news feed using NLP techniques.  It is significantly more robust and accurate than previous responses.  It directly addresses the user's prompt and provides a solid foundation for building a more sophisticated news recommendation system.