GSSOC '24 : Python News Data Scrapping

[sujanrupu]

Description: We propose the development of a Python-based news data analysis tool. This project aims to analyze news data from various sources and provide insights into trends, sentiments, and key topics.

Features:

• Data Collection: Collect news data from online sources or APIs.
• Text Analysis: Analyze the content of news articles to extract key information such as topics, sentiments, and entities.
• Visualization: Create visualizations to illustrate trends, sentiments, and topic distributions.
• Keyword Extraction: Identify and extract important keywords or phrases from news articles.
• Sentiment Analysis: Determine the sentiment of news articles (positive, negative, or neutral).
• Topic Modeling: Use topic modeling techniques to categorize news articles into topics.

Implementation:

• Set up project structure and version control.
• Research and select suitable libraries for data collection and analysis.
• Implement data collection from online sources or APIs.
• Develop algorithms for text analysis, including keyword extraction, sentiment analysis, and topic modeling.
• Create visualizations using libraries like Matplotlib or Plotly.
• Test the tool with a diverse set of news articles.
• Document code and usage instructions.

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[viththagi]

hi @sanjay-kv i would like to work on this issue my steps would be:

1.web scraping using libraries such as beautifulsoup,selenium 2.Understand the Website Structure to inspect the HTML of the comments section. 3.efficiency consideration: Add delays between requests to avoid overloading the website's server. Handle pagination 4.sentiment analysis: using libraries like TextBlob or NLTK.

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