World Happiness Index based on News Sentiment

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Steps to Follow

Here's a detailed step-by-step approach to completing the project on calculating the World Happiness Index based on news sentiment:

  1. Define Project Scope and Requirements:

    • Clearly define the project's objectives, including the calculation of the World Happiness Index based on news sentiment.
    • Identify the specific requirements, such as the news sources to crawl, sentiment analysis approach, data visualization, and any additional features.
  2. Gather News Data:

    • Identify the news sources from which to gather data. This could include popular news websites or specific domains related to happiness and well-being.
    • Develop a web crawler using Python to scrape news articles from the identified sources.
    • Implement a mechanism to retrieve news data on a daily basis, storing it in a structured format (e.g., CSV, JSON) along with the publication date.
  3. Perform Sentiment Analysis:

    • Preprocess the text data by removing unwanted characters, converting to lowercase, and performing other necessary cleaning steps.
    • Utilize a sentiment analysis technique to assign sentiment scores to each news article.
    • Assign sentiment scores ranging from -1 to 1, representing the negativity or positivity of the news.
    • Calculate the sentiment score for each news article and associate it with the respective publication date.
  4. Develop the Happiness Index Calculation:

    • Determine the weighting scheme for the sentiment scores based on their importance in determining happiness.
    • Sum the sentiment scores multiplied by their corresponding weights for each day.
    • Calculate the total weight by summing the weights assigned to each sentiment score.
    • Apply the formula: Happiness Index = (Sum of (Sentiment Score * Weight)) /Total Weight to calculate the happiness index for each day.
  5. Data Visualization:

    • Use Python libraries such as Matplotlib, Seaborn, or Plotly to create visualizations (charts, graphs) of the calculated happiness index over time.
    • Choose appropriate visualization types, such as line charts or bar graphs, to display trends and fluctuations in the index.
    • Provide interactive features if desired, allowing users to explore the data and gain insights.
  6. Optional Features:

    • Implement additional features based on project requirements and customization options.
    • These could include data aggregation at different levels (regional or global), customization of weights or sentiment thresholds, trend analysis, or a user-friendly interface.
  7. Testing and Validation:

    • Perform thorough testing of the project to ensure accurate data retrieval, sentiment analysis, and index calculation.
    • Validate the results by comparing them with existing happiness index reports or expert opinions.
  8. Documentation and Presentation:

    • Document the project, including its objectives, methodologies, and implementation details.
    • Prepare a presentation summarizing the project's findings, methodology, and results.

By following these steps, learners will be able to complete the project and gain hands-on experience in web scraping, sentiment analysis, data manipulation, formula development, and data visualization. They will have the opportunity to customize the project, explore additional features, and showcase their skills in analyzing and visualizing real-world data to calculate the World Happiness Index based on news sentiment.

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