shreekeshavan/sentiment_analysis

📝Implemented sentiment analysis in Python, classifying text data as positive🟩, negative🟥, or neutral🟨 using machine learning and visualization.💬🤖

Sentiment Analysis

This project demonstrates sentiment analysis using Python and various libraries. It aims to analyze and classify text data into positive, negative, or neutral sentiment.

Getting Started

To get started with this project, you'll need to set up a Jupyter notebook environment. We recommend using Google Colab for this purpose.

Prerequisite

Make sure you have the following library installed:

• pandas for data manipulation
• seaborn for data visualization
• wordcloud for generating word clouds
• matplotlib for creating plots
• sklearn for machine learning tasks

You can install this library using pip:

pip install pandas seaborn wordcloud matplotlib scikit-learn

Dataset

For this project, we'll be using a dataset of text data labeled with sentiment (positive, negative, or neutral).

Usage

1. Open the Jupyter notebook provided in this repository using Google Colab.

2. Load the dataset using pandas:

3. Perform data exploration and visualization using seaborn, wordcloud, and matplotlib.

4. Preprocess the text data and prepare it for machine learning. You can use techniques like tokenization and feature extraction with CountVectorizer.

5. Split the dataset into a training and testing set:

6. Train a sentiment analysis model using a machine learning algorithm like Multinomial Naive Bayes from sklearn:

7. Evaluate the model using metrics like a confusion matrix:

8. Visualize the results, generate word clouds for different sentiment categories, and interpret the findings.

9. Experiment with different machine learning algorithms and hyperparameters to improve model performance.

Contribution

• Contribution and insight are invaluable! Feel free to open an issue or pull request to provide feedback or enhancement.