With social media playing a central role in brand communications and customer engagement, it is crucial for businesses to analyze social media data effectively to optimize their marketing strategies. Traditional analysis methods are often labor-intensive and limited in scope. This project focuses on developing a comprehensive social media analytics framework powered by NLP and machine learning to conduct brand sentiment analysis, monitor engagement metrics, and generate actionable insights for marketing teams.
Week 1-2: Initial Planning and Requirement Analysis
Define the objectives, scope, and high-level requirements of the project
Accumulate the needed data and resources Week 3-4: Data Collection and Preparation Phase
Gather multi-platform social media data from their APIs
Clean and pre-process the data for analysis Week 5-6: Sentiment Analysis and Topic Modeling Phase
Develop NLP models for sentiment and topic modeling
Train the model on the social media data collected Week 7-8: Dashboard Development Phase
Design and develop a dashboard to visualize key metrics and insights
Integrate NLP models with data visualization tools.
Weeks 9-10: Testing and Refinement Phase
Extensive testing for accuracy and reliability
Refine the system given the performance metrics and user feedback.
Weeks 11-12: Final Evaluation and Reporting
Final evaluation and validation of the system.
Final project report and documentation compilation
Individual report presentations by students
