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TThe objective of this project is to analyze user-generated content across various platforms and derive insights from the sentiment (positive, negative) of the posts. The goal is to understand trends in user sentiment, identify key engagement metrics, and analyze the impact of sentiment on user behavior and platform performance over time.
SQL: For advanced querying, data aggregation, and analysis from a structured database of social media posts.
Window Functions: For advanced calculations like ranking, running totals, rolling averages, and year-over-year comparisons.
Subqueries & Common Table Expressions (CTEs): For breaking down complex queries and performing analysis on derived datasets.
Sentiment Analysis Tools: Integration of sentiment classification (positive, negative) for posts, possibly from a pre-trained machine learning model or rule-based sentiment analysis tool like VADER.
Data Visualization: Data insights can be visualized using tools like Matplotlib, Tableau, or Power BI to present trends in sentiment and engagement metrics.
Sentiment Classification: Posts are categorized as positive or negative, enabling a detailed analysis of sentiment trends over time.
Engagement Metrics: Likes, retweets, and comments are analyzed to assess engagement levels across different platforms.
User Activity & Influencer Identification: Identifies the most active users, top influencers, and users with high engagement rates.
Hashtag Analysis: Tracks and ranks the most common and trending hashtags associated with different sentiments.
Platform Comparison: Comparison of sentiment, engagement, and posting behavior across various social media platforms.
Country-wise Sentiment Distribution: Insights into how sentiment varies across different countries.
Peak Hour Identification: Identifies the hours of highest posting activity and engagement, providing insights into user behavior.
Year-over-Year Sentiment and Engagement Growth: Analyzes changes in sentiment and user engagement metrics (likes, retweets) over time.
Rolling Averages & Trends: Rolling averages are calculated to analyze sentiment shifts and sudden changes in user sentiment on a day-to-day basis.
Retention Analysis: Tracks user retention rates by identifying users who posted in previous years but were inactive in the current year.
Improved User Engagement Insights: The analysis provides actionable insights into user engagement patterns, allowing platforms and businesses to tailor their content strategy for maximum impact.
Understanding Sentiment Trends: Tracking sentiment trends over time enables the identification of events or triggers that caused sudden shifts in user sentiment, enabling more responsive brand strategies.
Platform and Country-specific Insights: Provides granular insights into user behavior on specific platforms and countries, allowing businesses to better understand regional preferences and tailor their marketing campaigns accordingly.
Influencer Identification: Identifies key influencers on each platform, enabling brands to partner with individuals who have the highest engagement levels.
Data-driven Decision Making: The detailed sentiment and engagement analysis help businesses and social media platforms make informed decisions based on user behavior, content performance, and sentiment trends.
Increased Retention and Engagement: Insights from user retention and peak posting times enable platforms to implement strategies that keep users engaged and active on their platforms.
