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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:

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

bash Copy code 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:

bash Copy code 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):

python Copy code 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 Copy code

Make sure to include a requirements.txt file with the necessary dependencies. Here’s an example:

streamlit
pandas
scikit-learn

rajeevsharma2024