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Sentiment Analysis Application

This repository contains a simple sentiment analysis application built using Streamlit. The application allows users to enter a review and check its sentiment (positive or negative) using a pre-trained machine learning model.

Features

• User input for review text.
• Real-time sentiment prediction.
• Simple and interactive web interface built with Streamlit.

Requirements

• Python 3.x
• Streamlit
• pandas
• scikit-learn
• pickle

Installation

1. Clone the repository:

   git clone https://github.com/machphy/sentiment-analysis-
   
   cd sentiment-analysis

Create a virtual environment and activate it:

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python -m venv venv
source venv/bin/activate # On Windows, use venv\Scripts\activate
Install the required packages:

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pip install -r requirements.txt
Place the model.pkl and scaler.pkl files in the project directory. These files should contain the pre-trained model and scaler, respectively.

Usage
Run the Streamlit application:

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streamlit run app.py
Open your web browser and navigate to http://localhost:8501 to interact with the application.

Project Structure
app.py: Main application file containing the Streamlit app code.
model.pkl: Pre-trained machine learning model.
scaler.pkl: Scaler used for preprocessing the input data.
requirements.txt: List of required Python packages.
Example Code
Here is the main code for the Streamlit application (app.py):

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import pandas as pd
import pickle as pk
from sklearn.feature_extraction.text import TfidfVectorizer
import streamlit as st

Load the pre-trained model and scaler

model = pk.load(open('model.pkl', 'rb'))
scaler = pk.load(open('scaler.pkl', 'rb'))

Streamlit app

st.title('Sentiment Analysis Application')

User input

rajeev = st.text_input('Enter and check sentiment')

if st.button('Predict'):
review_scale = scaler.transform([rajeev]).toarray()
result = model.predict(review_scale)
if result[0] == 0:
st.write('Negative Review')
else:
st.write('Positive Review')
License
This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments
Streamlit
scikit-learn
pandas
javascript
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Make sure to include a requirements.txt file with the necessary dependencies. Here’s an example:

streamlit
pandas
scikit-learn

rajeevsharma2024