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https://github.com/vidit-kushwaha/tweet-sentimental-analysis
A deep learning project for sentiment analysis on tweets, classifying them into positive, negative, or neutral sentiments.
https://github.com/vidit-kushwaha/tweet-sentimental-analysis
deep-learning keras machine-learning natural-language-processing python sentiment-analysis tensorflow text-classification
Last synced: 3 months ago
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A deep learning project for sentiment analysis on tweets, classifying them into positive, negative, or neutral sentiments.
- Host: GitHub
- URL: https://github.com/vidit-kushwaha/tweet-sentimental-analysis
- Owner: Vidit-Kushwaha
- License: mit
- Created: 2024-09-03T17:45:29.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-04T18:49:57.000Z (4 months ago)
- Last Synced: 2024-10-13T00:41:23.016Z (3 months ago)
- Topics: deep-learning, keras, machine-learning, natural-language-processing, python, sentiment-analysis, tensorflow, text-classification
- Language: Jupyter Notebook
- Homepage:
- Size: 1.44 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Sentiment Analysis on Tweets
## Table of Contents
- [Introduction](#introduction)
- [Project Overview](#project-overview)
- [Features](#features)
- [Model Architecture](#model-architecture)
- [Data Interpretation and Preprocessing](#data-interpretation-and-preprocessing)
- [Training the Model](#training-the-model)
- [Results](#results)
- [Installation](#installation)
- [Contributing](#contributing)
- [License](#license)
## Introduction
This project is a sentiment analysis model applied to tweets. It uses machine learning and natural language processing (NLP) techniques to classify the sentiment of tweets into three categories: positive, negative, and neutral. The project demonstrates the use of various preprocessing steps, TF-IDF vectorization, and a deep learning model built with TensorFlow and Keras.
## Project Overview
Sentiment analysis is a key aspect of NLP that involves determining the sentiment expressed in a piece of text. This project leverages the power of deep learning and TF-IDF vectorization to perform sentiment analysis on a dataset of tweets.
## Features
- **Data Preprocessing**: Clean and preprocess raw tweet data, including removing stopwords and lemmatization.
- **TF-IDF Vectorization**: Convert the cleaned text data into numerical form using TF-IDF.
- **Deep Learning Model**: Train a neural network model to classify sentiments.
- **Visualization**: Plot accuracy and loss metrics to evaluate model performance.
## Model Architecture
The model used in this project is a sequential neural network with the following layers:
- **Dense Layer**: 100 units with ReLU activation
- **Dropout Layer**: 0.3 dropout rate
- **Dense Layer**: 25 units with ReLU activation
- **Dropout Layer**: 0.3 dropout rate
- **Dense Layer**: 10 units with ReLU activation
- **Dropout Layer**: 0.3 dropout rate
- **Output Layer**: 3 units with softmax activation for multiclass classification
## Data Interpretation and Preprocessing
We have an image depicting a dataframe and a list of features. We will utilize the 'text' feature as input and consider the 'sentiment' feature as our target variable. Our goal is to predict the likelihood of a text being categorized as positive, negative, or neutral.
The data preprocessing pipeline includes:
- **Lowercasing**: Convert all text to lowercase.
- **Contraction Expansion**: Expand contractions (e.g., "can't" to "cannot").
- **Special Character Removal**: Remove non-alphanumeric characters.
- **Tokenization**: Split text into words.
- **Stopword Removal**: Remove common stopwords.
- **Lemmatization**: Reduce words to their base or root form.
## Training the Model
The model is trained using the following configuration:
- **Loss Function**: Categorical Crossentropy
- **Optimizer**: Adam with a learning rate of 0.01
- **Metrics**: Accuracy
- **Epochs**: 10
## Results
The performance of the model is evaluated using accuracy and loss plots. The final trained model is able to classify tweet sentiments with a reasonable accuracy.
## Installation
To run this project locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/Vidit-Kushwaha/Tweet-Sentimental-Analysis.git
cd Tweet-Sentimental-Analysis
```
2. Run the Jupyter Notebook:
```bash
jupyter notebook Sentiment_Analysis_on_Tweets.ipynb
```
Train the model: Follow the steps in the notebook to preprocess the data, train the model, and visualize the results.
## Contributing
We welcome contributions! Whether you're a seasoned developer or a curious enthusiast, there are ways to get involved:
- **Bug fixes and improvements:** Find any issues? Submit a pull request!
- **New features:** Have an idea for a cool feature? Let's discuss it in an issue!
- **Documentation:** Improve the project's documentation and website.
- **Spread the word:** Share the project with your network and help it grow!
You can follow standard python contribution guidelines.
## License
This project is licensed under the MIT License. See the [LICENSE](https://github.com/Vidit-Kushwaha/Tweet-Sentimental-Analysis/blob/main/LICENSE) file for details.