Sentiment Analysis Using a Deep Convolutional Neural Network

Overview

This sentiment analysis program utilizes a Deep Convolutional Neural Network (CNN) to analyze the sentiment of user input. The CNN architecture is designed to extract features from input text and make predictions about the sentiment expressed in the text. To run the program, follow the instructions below.

Requirements

• Python 3.x
• NumPy
• Pandas
• BeautifulSoup4
• TensorFlow
• Keras
• TensorFlow-Datasets

All required libraries and frameworks can be found at the top of sentiment_analysis.py.

Installation

Clone the repository to your local machine using git clone https://github.com/RochanVanam/sentiment_analysis.git.

Usage

1. Make sure you have all the requirements properly installed.
2. Run sentiment_analysis.py.
3. The program will prompt you for a phrase for sentiment analysis. Enter a phrase.
4. To stop the program, simply stop running it or exit the program.

Data

The program uses a dataset train.csv located in the data/ directory. It is a csv file containing millions of tweets and their sentiments. The data was provided through a Udemy course listed below.

Acknowledgments and Credits

This program was developed as part of the instructional content provided by a Udemy course titled "Modern Natural Language Processing," imparting valuable knowledge and practical insights into sentiment analysis techniques using Deep Convolutional Neural Networks (CNN).

Udemy Course Info

• Course Title: Modern Natural Language Processing
• Instructor/Creators: Martin Jocqueviel, SuperDataScience Team, Ligency Team
• URL: https://www.udemy.com/course/modern-nlp/

Thank you!

Rochan V:

• GitHub
• LinkedIn