A web-based interface that analyzes customer reviews using TextBlob for sentiment classification (positive, negative, or neutral). Built with Streamlit, it supports real-time text analysis and batch processing via CSV uploads.
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Analyze individual text reviews for polarity and subjectivity.
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Clean text input using
cleantext. -
Upload and analyze CSV files containing reviews.
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Download the analyzed CSV file with sentiment scores and analysis.
- Customer Feedback Analysis: Businesses can analyze customer reviews to understand their sentiments towards products or services, helping them improve customer satisfaction.
- Market Research: Companies can use sentiment analysis to gauge public opinion about their brand or competitors, aiding in strategic decision-making.
- Product Improvement: By analyzing reviews, businesses can identify common issues or areas for improvement in their products.
- Brand Monitoring: Sentiment analysis helps in monitoring brand reputation by analyzing social media posts, reviews, and other online content.
- Customer Support: Sentiment analysis can be used to prioritize customer support tickets based on the sentiment expressed, ensuring timely resolution of negative feedback.
Polarity: The polarity score ranges from -1 to 1.
- A score closer to 1 indicates positive sentiment.
- A score closer to -1 indicates negative sentiment.
- A score around 0 indicates neutral sentiment.
Subjectivity: The subjectivity score ranges from 0 to 1.
- A score closer to 1 indicates subjective text (personal opinions, emotions, etc.).
- A score closer to 0 indicates objective text (factual information).
Sentiment-Analysis.Video-Output.mp4
- Clone the repository:
git clone https://github.com/rambabu-akkapolu/sentiment-analysis.git cd sentiment-analysis - Create a virtual environment:
python -m venv venv
- Activate the virtual environment:
venv\Scripts\activate
- Install the required dependencies:
pip install -r requirements.txt
- Usage - Run the Streamlit app:
streamlit run main.py