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The Reliability of the news of the news is distinguished by a percentage
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News is classified using Ai
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Models
Model1: News classification
Objective:
Classification of news written by the writer (political, economic, sports, etc.)
How it works
Receive the written text from the writer.
Classify the news according to the specified patterns
Analyze the text using the classification model.
Model outputs
News type
input
News Articles (Text)
Preprocessing:
Text extraction
Text vectorization
Steps
Text Vectorization:
Convert the news article into a structured format using TfidfVectorizer.
Model Training:
Use a classification algorithm like Logistic Regression, Naive Bayes, or Random Forest.
Train using labeled data (e.g., categories like Politics, Sports, Science).
Evaluation:
Use accuracy, classification report, confusion matrix for evaluation.
Model 2:Article Credibility and Reliability Prediction
Steps:
Data Extraction:
Scrape and extract data from multiple reliable sources
Feature Extraction:
Extract features like text sentiment, consistency of facts, tone, and bias.
Model:
Train a Logistic Regression or Random Forest model using labeled data (e.g., factual, biased).
Evaluation:
Use metrics like accuracy, F1-score, confusion matrix to evaluate credibility.
Input:
News Articles (Text)
Reliable Sources (BBC, Al Jazeera, Reuters, Time)
Preprocessing:
Extract article content from reliable news sources.
Compare the same news across sources.
Output:
Reliability score (percentage) indicating how credible the article is based on comparison with the trusted sources.
Objective:
Assess the credibility and reliability of an article based on comparison across four reliable news sources.
Model 3:Article Generation from Reliable News Sources
Objective:
Generate new articles using text data scraped from reliable sources.
Input:
List of reliable sources (e.g.,. BBC, Al Jazeera, Reuters, Time)
Preprocessing:
Scrape and clean articles from multiple reliable sources.
Steps:
Text Generation:
Use GPT-like models or Transformer-based models to generate new articles based on the scraped content.
Fine-tune using pre-trained models on news datasets.
Evaluation:
Use metrics like BLEU score, ROUGE score for evaluating the generated content's quality.
Data Extraction:
Scrape news articles from multiple sources
Output:
New articles generated based on trusted news sources.