/SentimentAnalyzer

This Django web application performs sentiment analysis on user-provided sentences. It allows users to input sentences and analyzes the sentiment of the input using a sentiment analysis module.

Primary LanguagePythonMIT LicenseMIT

Django Sentiment Analysis Web App

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This Django web application performs sentiment analysis on user-provided sentences. It allows users to input sentences and analyzes the sentiment of the input using a sentiment analysis module.

Features

  • Input Sentence Analysis: Users can input a sentence which will be analyzed for sentiment.
  • Sentiment Analysis: The application performs sentiment analysis on the input sentence.
  • Tokenization and Stopword Removal: Before analysis, the input sentence is tokenized and stopwords are removed.
  • Part-of-Speech Tagging: The application also provides part-of-speech tagging for the words in the input sentence.
  • Frequency Distribution: It calculates and displays the frequency distribution of words in the input sentence.

Installation

git clone <repository-url>
cd <project-directory>
pip install -r requirements.txt

Usage

  1. Run the Django server:

    python manage.py runserver

  2. Access the application in your web browser at http://localhost:8000/.

  3. Enter a sentence in the provided input field and submit for analysis.

  4. View the sentiment score, tokenized words, stopwords removed, part-of-speech tagging, and frequency distribution of words in the analyzed sentence.

Files and Structure

  • analyse/ directory:
    • forms.py: Contains the form definition for user input.
    • templates/analyse/ directory: Contains HTML templates for rendering views.
      • index.html: Template for the homepage with the input form.
      • analyse.html: Template for displaying the analysis results.
  • sentiment_analyser.py: Module for sentiment analysis and related functions.
  • phrase.json: JSON file to store input sentences and their details.

Dependencies

  • Django
  • Python (>=3.x)

Contribution Guidelines

Contributions to the repository are welcome. If you find any issues or would like to add new features, please follow these guidelines:

  • Fork the repository and create a new branch for your feature or bug fix.
  • Ensure that your code follows PEP 8 style guidelines.
  • Submit a pull request with a clear description of the changes you have made.

Contribution Guidelines.

License

This project is licensed under the MIT License.

Contact

For any questions or inquiries, please contact meghamgarg@gmail.com.