Digital Diary with Sentiment Analysis

Overview

This is a simple command-line Python application that allows users to write daily journal entries. Each entry is analyzed for sentiment using the TextBlob library to classify it as positive, negative, or neutral. The app helps users track their emotional well-being over time by providing sentiment summaries and allowing them to view and search past entries.

Features

• Add New Entry: Write a journal entry, which is analyzed for sentiment.
• View All Entries: See all past journal entries with sentiment feedback.
• Search Entries: Search through entries by keywords.
• Mood Summary: View a summary of moods (positive, negative, neutral) from all entries.
• Persistent Storage: Entries are saved in a text file for future access.

Prerequisites

Before running this project, make sure you have the following installed:

• Python 3.x
• TextBlob library

To install TextBlob, run:

pip install textblob

Usage

1. Clone the repository or download the project files.
2. Navigate to the project folder and run the script:

python diary.py

3. Follow the on-screen prompts to add, view, search, or summarize journal entries.

File Structure

• diary.py: Main Python script containing the application logic.
• diary.txt: Text file where all journal entries are stored (created automatically).

Example Usage

1. Add New Entry:

   • You will be prompted to write a journal entry, which will be analyzed for sentiment.
   • Example:

     Write your journal entry: Today was a great day!

2. View All Entries:

   • Display all past journal entries with sentiment feedback.
   • Example:

     Date: 2024-12-04 10:00:00
     Mood: Positive
     Entry: Today was a great day!

3. Mood Summary:

   • View a breakdown of your emotional moods over time.
   • Example:

     Positive: 3 (60.0%)
     Negative: 2 (40.0%)
     Neutral: 0 (0.0%)

How Sentiment Analysis Works

• The sentiment analysis is performed using the TextBlob library, which evaluates the text's polarity to classify it as:
  • Positive: Sentiment score greater than 0
  • Negative: Sentiment score less than 0
  • Neutral: Sentiment score equal to 0

Contributions

Feel free to fork the repository and contribute by opening issues, submitting pull requests, or suggesting features.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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This README provides an overview, installation instructions, usage examples, and a brief explanation of how the sentiment analysis works. You can further customize it based on the specific details of your project.