Analyze and visualize sentiment patterns in social media data to understand public opinion and attitudes towards specific topics or brands.
This project aims to perform sentiment analysis on social media data and predict sentiment labels using logistic regression. The sentiment labels could be binary (positive/negative) or multi-class (positive/neutral/negative), depending on the dataset.
Social media platforms are rich sources of textual data, which can provide valuable insights into public sentiment and opinion towards various topics or brands. This project utilizes machine learning techniques, specifically logistic regression, to analyze social media text and predict sentiment labels.
- Data Preprocessing: The provided dataset is preprocessed to clean the text, remove noise, and prepare it for analysis.
- Sentiment Analysis: Textual data is analyzed to determine sentiment scores or labels using a logistic regression model.
- Model Evaluation: The performance of the logistic regression model is evaluated using metrics such as accuracy, precision, recall, and F1-score.
- Visualization: Confusion matrices and classification reports are visualized to interpret the model's performance.
- Python 3.x
- Libraries: pandas, numpy, matplotlib, scikit-learn, etc.