Sentiment Studio

Sentiment Studio is an web application for analyzing the sentiment of product reviews using both classical machine learning models and pretrained sentiment analysis tools.

---

Features

📝 Single Review Analysis

• Enter a product review in a text box.
• Get sentiment predictions from four models:
  • Logistic Regression
  • Naive Bayes
  • VADER
  • TextBlob

📁 Batch Review Analysis

• Upload a CSV or Excel file containing a column named reviews.text.
• The app will analyze all reviews in the file using the VADER model.
• View:
  • Sentiment distribution (pie chart and summary stats)
  • Color-coded table of all reviews and their predicted sentiment
  • Option to download the results as a CSV file

Getting Started

1. Clone the Repository

git clone https://github.com/umerr1214/sentiment_analyzer.git
cd sentiment_analyzer

2. Install Requirements

pip install -r requirements.txt

3. Run the App

streamlit run app.py

---

Usage

Single Review

• Enter your review in the text box.
• Select which model(s) to use from the sidebar.
• Click Analyze Sentiment to see predictions, explanations, and visualizations.

Batch Analysis

• Go to the sidebar and upload a .csv or .xlsx file with a column named reviews.text.
• Click Analyze All Reviews (VADER).
• View the results, download the output, and explore the sentiment distribution.

---

File Format for Batch Analysis

• The file must have a column named reviews.text.
• Supported formats: .csv, .xlsx, .xls
• Example:

reviews.text
This product is amazing!
Not worth the price.
It's okay, does the job.

---

Requirements

• Python 3.7+
• Streamlit
• pandas
• scikit-learn
• nltk
• vaderSentiment
• textblob
• plotly
• openpyxl

(Install all dependencies with pip install -r requirements.txt)

---

Enjoy analyzing your product reviews with Sentiment Studio!