This project is a Streamlit web application that extracts text from Wikipedia articles and provides summarization along with optional sentiment analysis and entity recognition.

• Extract text from Wikipedia articles by providing either a topic title or a Wikipedia page URL.
• Customize analysis with options for sentiment analysis and entity recognition.
• Display word cloud visualization of extracted text.
• Summarize the extracted text.
• Perform sentiment analysis on the extracted text (optional).
• Recognize entities in the text (optional).

To run the application, make sure you have Python installed on your system along with the required dependencies listed in requirements.txt.

pip install -r requirements.txt
streamlit run app.py

or

CLICK ON ME TO PLAY WITH A REAL LIVE VERSION. 😶‍🌫️

The application will launch in your default web browser. You can then go ahead and interact with it using the provided user interface.

The application offers customization options through the sidebar:

• Choose an option: Select whether you want to enter a topic title or a Wikipedia page URL.
• Customize Your Analysis: Toggle sentiment analysis and entity recognition options.

The BART model powers the text summarization module fine-tuned for summarization tasks. The summarizer.py module contains the code for generating text summaries.

The utils.py module contains utility functions used in the project:

• write_word_cloud(text): Generates a word cloud visualization of the input text.
• sentiment_report(text): Provides a sentiment analysis report for the input text.
• get_entities(text): Recognizes entities in the input text.

The wikipedia_extractor.py module contains functions for extracting text from Wikipedia articles.

• GUI Interface: This screenshot shows the graphical user interface (GUI) of the application, where users can interact with the various features and functionalities.
• Word Cloud Visualization: This screenshot displays the word cloud visualization generated from the extracted text, providing a visual representation of the most frequently occurring words.
• Text Summary: This screenshot presents the summarized version of the extracted text, allowing users to quickly grasp the key points and main ideas.
• Sentiment Analysis Report: This screenshot exhibits the sentiment analysis report generated for the extracted text, indicating the overall sentiment (positive, negative, or neutral) and providing insights into the polarity and subjectivity.
• Named Entity Recognition (NER): This screenshot showcases the named entity recognition (NER) results, highlighting and categorizing entities such as persons, organizations, locations, etc., identified within the text.

• Streamlit
• spaCy
• NLTK
• matplotlib
• wordcloud
• TextBlob
• transformers 🤖

Moatasem Mohammed