The Text Monger is a Python package that provides various text analysis tools, including readability scoring, power words distribution, named entity recognition (NER), and sentiment analysis.
- Readability Analysis: Analyze the readability of text using various metrics such as Flesch Reading Ease, Gunning Fog Index, and more.
- Power Words Distribution: Visualize the distribution of power words in your text, highlighting impactful language.
- Named Entity Recognition (NER): Extract and highlight named entities (like names, organizations, locations) from the text.
- Sentiment Analysis: Determine the sentiment polarity (positive, negative, or neutral) and subjectivity of the text using
TextBlob
.
You can install The Text Monger package using pip
:
pip install textmonger
After installation, you can run The Text Monger from the command line. Here's how you can use it:
To analyze a piece of text, simply run the following command in your terminal:
textmonger
You will be prompted to enter the text you want to analyze. Type or paste your text, and when you're done, type END
on a new line to finish input. The tool will then output the readability analysis, power words distribution, named entity recognition, and sentiment analysis.
$ textmonger
Enter Text to analyze (type 'END' on a new line to finish):
The quick brown fox jumps over the lazy dog.
END
================================================================================
Readability Analysis
================================================================================
| Metric | Score |
| ---------------------------- | ------------------ |
| Reading ease | Difficult |
| Reading level | Grade 14.4 |
| Smog index | Grade 15.8 |
| Gunning Fog index | Grade 15.46 |
| Coleman-Liau index | Grade 13.06 |
| Automated Readability index | Grade 16.4 |
| Dale-Chall Readability score | 9.88 |
| Text standard | 15th and 16th grade |
================================================================================
Power Words Distribution
================================================================================
<Your Output Here>
================================================================================
Sentiment Analysis
================================================================================
| Polarity | 0.0 (Neutral) |
| Subjectivity | 0.5 (Subjective) |
================================================================================
================================================================================
Named Entity Recognition (NER)
================================================================================
<Your Output Here>
This project is licensed under the MIT License - see the LICENSE file for details.
Contributions are welcome! Please fork the repository and submit a pull request.
- Author: Sahil Garje
- Email: [email protected]