This Python script provides a detailed sentiment analysis of text. It goes beyond a simple mood classification by calculating and displaying two key metrics: Polarity (how positive or negative the text is) and Subjectivity (how much of an opinion it is vs. a fact).

This tool is built using the TextBlob library for its simplicity and power in natural language processing.

• Mood Classification: Classifies sentences into Happy 😊, Sad 😢, or Neutral 😐.
• Polarity Score: Provides a numeric score from -1.0 (most negative) to +1.0 (most positive).
• Subjectivity Score: Provides a numeric score from 0.0 (very objective) to 1.0 (very subjective).
• Detailed Output: Displays all analysis results in a clean, readable format.

1. Prerequisites: Make sure you have Python 3.x installed.

2. Install the library: This project requires the TextBlob library. Install it using pip:

   pip install textblob

To run the analysis on the example sentences, simply execute the script from your terminal:

python main.py

You can customize the sentences to be analyzed by editing the sentences_to_test list directly in the main.py file.

The script provides two main scores for each sentence:

• Polarity: This score represents the emotional leaning of the sentence. A positive score means a happy sentiment, a negative score means a sad sentiment, and a score near zero is neutral.
• Subjectivity: This score measures whether the sentence is more of a factual statement or a personal opinion. A score of 0.0 means it's very objective, while a score of 1.0 means it's very subjective.

--- Detailed Mood Summary ---
Sentence: 'I am incredibly happy and excited about this new project!'
  - Mood: Happy 😊
  - Polarity: 0.92
  - Subjectivity: 0.95

Sentence: 'The Earth is the third planet from the Sun.'
  - Mood: Neutral 😐
  - Polarity: 0.00
  - Subjectivity: 0.00

Sentence: 'I think this is the best movie ever made.'
  - Mood: Happy 😊
  - Polarity: 0.75
  - Subjectivity: 0.45