Leveraging NLP to extract deep insights from group chats, enhancing user engagement and business opportunities.
Skillset used: Natural Language Processing (NLP), Sentiment Analysis, Regex, Bag of Words (BoW), TextBlob, VADER, Data Visualization (Tableau), Cosine Similarity
🔍 What I did
- Developed an NLP-powered analysis of a WhatsApp group with 200+ members to assess sentiment, engagement trends, and business opportunities.
- Implemented RegEx, BoW, TextBlob, and VADER Sentiment Analysis on 5,000+ messages to examine group dynamics and user sentiment.
- Created an interactive Tableau dashboard visualizing:
- User message trends (top contributors, silent observers)
- Peak activity hours (optimal times for engagement)
- Message sentiment distribution (positive, neutral, negative interactions)
📈 Impact & Insights
- Mental Health Awareness: Identified users who frequently express negative sentiment, enabling proactive engagement and support.
- Personalized Networking: Recommended like-minded connections based on sentiment and conversation themes.
- Business Growth: Insights from chat sentiment helped businesses optimize ad placements, increasing revenue potential.
- Community Building: Delivered a comprehensive group analysis to enhance interactions, improving overall engagement.
🚀 Learning Outcomes
- Gained hands-on experience in text processing & NLP for real-world applications.
- Strengthened skills in data visualization & storytelling using Tableau.
- Explored sentiment-based recommendations and their impact on business & user engagement.
- Understood the role of AI in social media & communication analysis.