BSc IT Project Guide: Product Launch Sentiment Analysis
1. Introduction
Product launches are critical events for companies, as public perception can significantly influence the success of a product. This project aims to develop a system that monitors and analyzes public sentiment from various sources such as social media, forums, and news outlets during product launches. The objective is to provide real-time insights into how a new product is being received by the public.
2. Objectives
- To collect data from social media platforms and other
public forums during product launches.
- To perform sentiment analysis on collected data.
- To visualize the results to identify trends and insights.
- To generate real-time alerts and reports on sentiment changes.
3. Tools and Technologies
- Programming Language: Python
- Libraries: Tweepy, TextBlob, NLTK, scikit-learn, pandas, matplotlib
- Database: MongoDB or MySQL
- Frontend: HTML, CSS, JavaScript
- Backend: Flask or Django
- Visualization: Plotly, Dash, or D3.js
4. System Architecture
The system will include the following components:
1. Data Collection Module
2. Data Preprocessing Module
3. Sentiment Analysis Engine
4. Visualization Dashboard
5. Notification and Reporting Module
5. Methodology
1. Collect real-time data using APIs (e.g., Twitter API).
2. Preprocess data to clean and normalize text.
3. Apply sentiment analysis models to classify sentiments.
4. Store analyzed data in a database.
5. Display results on an interactive dashboard with trend lines and sentiment
scores.
6. Generate reports for analysis and decision-making.
6. Expected Outcome
A working web-based tool that:
- Monitors sentiment during product launches in real-time
- Provides sentiment trends and visual analytics
- Alerts stakeholders on significant sentiment changes
- Aids in post-launch analysis and strategy adjustment
7. Future Enhancements
- Multi-language sentiment analysis
- Integration with more data sources (Reddit, Instagram)
- Emotion classification beyond sentiment (anger, joy, etc.)
- Predictive analytics for future product success