Sentiment analysis on Twitter involves analyzing tweets to determine the sentiment expressed within them, typically categorized as positive, negative, or neutral. This process often utilizes natural language processing (NLP) techniques and machine learning algorithms to automatically classify the sentiment of a tweet. These are the essential features that are required for the project
- Data Collection
- Preprocessing
- Feature Extraction
- Sentiment Classification
- Evaluation
- Deployment
- Visualization and Interpretation.
Overall, sentiment analysis on Twitter is a valuable tool for businesses, researchers, and organizations to gain insights into public opinion, brand sentiment, and emerging trends in real-time. It can be used for various applications including brand monitoring, customer feedback analysis, market research, and crisis management.