Create a simple journal entry analyzer

Here’s a simple Journal Entry Analyzer implemented in Python. It analyzes a journal entry to extract:

• Sentiment (positive/negative/neutral)

• Word count

• Most common words (excluding stopwords)

• Date and time of entry (automatically added)

python
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import datetime
import re
from collections import Counter
from textblob import TextBlob
import nltk
from nltk.corpus import stopwords

# Download stopwords if not already done
nltk.download('stopwords')

def analyze_journal_entry(entry: str):
    # Capture current date and time
    entry_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    # Basic cleaning
    cleaned_entry = re.sub(r'[^ws]', '', entry.lower())

    # Tokenize and remove stopwords
    words = cleaned_entry.split()
    filtered_words = [word for word in words if word not in stopwords.words('english')]

    # Word count
    word_count = len(words)

    # Most common words
    word_freq = Counter(filtered_words).most_common(5)

    # Sentiment analysis using TextBlob
    blob = TextBlob(entry)
    polarity = blob.sentiment.polarity

    if polarity > 0.1:
        sentiment = "Positive"
    elif polarity < -0.1:
        sentiment = "Negative"
    else:
        sentiment = "Neutral"

    # Output results
    print("Journal Entry Analysis")
    print("======================")
    print(f"Date and Time: {entry_time}")
    print(f"Word Count: {word_count}")
    print(f"Sentiment: {sentiment} (Polarity Score: {polarity:.2f})")
    print("Most Common Words:")
    for word, freq in word_freq:
        print(f" - {word}: {freq} time(s)")

# Example usage
if __name__ == "__main__":
    journal_text = input("Enter your journal entry:n")
    analyze_journal_entry(journal_text)

Features:

• Uses TextBlob for sentiment analysis

• Uses NLTK for stopword filtering

• Automatically timestamps entries

• Designed to be simple and extendable

Let me know if you want a version with a GUI or web-based interface.