Sentiment Analysis through Deep Learning with Keras and Python

Description

Learn to apply sentiment analysis to your problems through a practical, real-world use case.

Do you want to learn how to perform sentiment analysis? The answer should almost always be yes if you are working in any business domain. Every company on the face of the earth wants to know what its customers feel about its products and services - and sentiment analysis is the easiest and most accurate way of finding out the answer to this question. By learning to perform sentiment analysis, you will make yourself invaluable to any company, especially those who are interested in quality assurance of their products and those working with business intelligence; and this is almost all sensible companies, large and small, nowadays. In this course, we make it easy to perform sentiment analysis. In the very first video, we introduce a sentiment analysis engine of fewer than 60 lines that can perform industry-grade sentiment analysis. We then spend the rest of the course explaining these very powerful 60 lines so that you have a thorough understanding of the code. After you are done with this course, you will immediately be able to plug this system into your existing pipelines to perform sentiment analysis of any text you can throw at it. That is one of the reasons you should use Python for sentiment analysis and not some other data science language such as R. If you work with R for sentiment analysis, you still have to put in a lot of effort to take this skill to the market. If you write your sentiment analysis engine in Python, incorporating your code into your final business product is dead easy. The second important tip for sentiment analysis is that the latest success stories do not try to do it by hand. Instead, you train a machine to do it for you. That is why we use deep sentiment analysis in this course: you will train a deep-learning model to do sentiment analysis for you. That way, you put in very little effort and get industry-standard sentiment analysis - and you can improve your engine later by simply utilizing a better model as soon as it becomes available with little effort. All the code files are placed at
https://github.com/PacktPublishing/Sentiment-Analysis-through-Deep-Learning-with-Keras-and-Python

Understanding how to write industry-grade sentiment analysis engines with very little effort
Basics of machine learning with minimal math
Understand not only the theoretical and academic aspects of sentiment analysis but also how to use it in your field - real-world sentiment analysis
Tips on avoiding mistakes made by new-comers to the field and the best practices to get you to your goal with minimal effort

Anyone who wants to perform sentiment analysis in the real world
Anyone who wants to understand how deep learning can help with sentiment analysis
Anyone who is working for a company that wants to see how its products are doing with their customers

This course is designed with the step-by-step approach and covers industry-grade sentiment analysis engines topics and best practices to get you to your goal with minimal effort.

You will learn to perform deep sentiment analysis the easy way * You will use Python to perform sentiment analysis * Using Python will allow you to integrate sentiment analysis in your existing solutions

https://github.com/packtpublishing/sentiment-analysis-through-deep-learning-with-keras-and-python

Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model. He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!

1. Introduction

2. Bare Essentials of Theory

3. Getting Started with Keras

4. Sentiment Analysis Case Study

5. Convolutional Neural Networks with Keras

6. Revisiting the Sentiment Analysis Model

7. Finishing Up