Social Media Analytics

Skills, Tech Stack, and Libraries

1. Skills: Sentiment Analysis, Real-Time Data Processing, Data Visualization, API Integration

2. Tech Stack: Python, SQL, Tableau, Power BI

3. Libraries: Pandas, NumPy, Matplotlib, Seaborn, Tweepy, NLTK, Scikit-learn

Description and Approach

Objective:

I built a real-time social media analytics system to track trending topics, analyze sentiment, and provide actionable insights for brands to improve their online presence and customer engagement.

Approach:

1. Data Collection:

   • Integrated with the Twitter API using Tweepy to collect live tweets based on hashtags and keywords relevant to the brand or topic.

   • Stored the collected tweets in a structured format using Pandas for processing and analysis.

2. Data Preprocessing:

   • Cleaned the tweet text by removing stop words, special characters, and URLs using NLTK.

   • Tokenized and normalized the text data for sentiment analysis.

3. Sentiment Analysis:

   • Applied a pre-trained sentiment analysis model from NLTK’s Vader or fine-tuned a Transformer-based model using Hugging Face to classify tweets as positive, negative, or neutral.

   • Aggregated sentiment scores to provide insights on overall brand sentiment.

4. Trending Topics Identification:

   • Performed keyword extraction using TF-IDF to identify trending topics and hashtags.

   • Visualized the frequency of keywords to highlight dominant themes.

Dashboard Design:

Built an interactive dashboard in Tableau to present:

1. Real-time sentiment scores.

2. Trends in tweet volume over time.

3. Geographic distribution of tweets for targeted insights.

Automation:

Set up an automated pipeline to continuously fetch and process tweets, ensuring the dashboard remained updated with live data.

Code Flow:

1. Connect to Twitter API using Tweepy and stream tweets in real time.

2. Clean and preprocess text data using NLTK.

3. Perform sentiment analysis and extract trending keywords.

4. Store processed data in SQL or a cloud-based database.

5. Visualize results in Tableau for stakeholders.

Results

The project successfully delivered a real-time social media analytics system with the following outcomes:

• Improved Customer Engagement: Identified customer pain points and sentiments, enabling brands to respond faster to feedback.

• Trending Topic Insights: Highlighted emerging topics, helping the marketing team align campaigns with current trends.

• Actionable Insights: Provided geographic and temporal breakdowns of social media activity, aiding targeted outreach strategies.

• Increased Brand Awareness: Helped stakeholders monitor the impact of campaigns and adjust strategies in real time.

  
  

This project showcased the application of advanced analytics in social media monitoring, enhancing brand visibility and customer relationships.

Git Link

For more information and code, visit the Git link.