ReviewSentinel

Sentiment Analysis of Brand Reviews

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.

Features

• Analyze individual text reviews for polarity and subjectivity.
• Clean text input using cleantext.
• Upload and analyze CSV files containing reviews.

• Download the analyzed CSV file with sentiment scores and analysis.

  

Real-World Applications

• 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.

Output

Polarity and Subjectivity

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).

https://github.com/user-attachments/assets/253fac49-7aff-407d-9ff4-bb7059d08b54

Installation

1. Clone the repository: ```sh git clone https://github.com/rambabu-akkapolu/sentiment-analysis.git cd sentiment-analysis
2. Create a virtual environment: ```sh python -m venv venv
3. Activate the virtual environment: ```sh venv\Scripts\activate
4. Install the required dependencies: ```sh pip install -r requirements.txt
5. Usage - Run the Streamlit app: ```sh streamlit run main.py

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