An overview of sentiment analysis

- [Instructor] Hi, and welcome to this course on Deep Learning with Python and Keras: Build a Model for Sentiment Analysis. Now, a large portion of this course will be a hands-on demo, where you'll actually train dense neural networks and recurrent neural networks to perform sentiment analysis on some financial data. But before we get there, there are some concepts that we need to cover, starting with what exactly is a sentiment analysis. Let's define this term first. It's a computational process of identifying and categorizing opinions expressed in a piece of text to determine whether the writer's attitude towards the topic is positive, negative, or neutral. These are the broad categories for sentiment analysis. You could, of course, have more granular categories if you wanted to, like very positive or mildly positive, and so on. So what do you do when you perform a sentiment analysis? You'll analyze text available in digital form to determine the emotional tone of the message. Now, organizations have been collecting all kinds of text for all kinds of reasons for a long time. This text can include emails, customer support chat transcripts, social media comments, or even reviews. All of this text contains some information that will be useful to the organization. Sentiment analysis tools can automatically scan text to determine the writer's attitude and tone, and this can give organizations important insights into their customers. So how can sentiment analysis tools help businesses? They offer businesses objective and consistent results in evaluating customer opinions, reducing personal bias that might occur with human reviewers. This allows organizations understand diverse customer perspectives and learn how customers respond to their products. These tools can also help organizations enhance their products and services. By identifying specific entities and sentiments associated with them, sentiment analysis enables companies to pinpoint areas for improvement in their products and services. These tools enable scalability in data analysis. There is a vast amount of structured data generated from various sources, emails, surveys, customer feedback. Sentiment analysis tools allow businesses to efficiently process and analyze this data at scale, helping them stay informed about customer opinions and trends. And real-time sentiment analysis empowers businesses to quickly respond to emerging trends or potential crises. Businesses can receive instant alerts on negative sentiments and take proactive measures to address customer concerns. So where could businesses use sentiment analysis in improving their customer service? This allows customer support teams to personalize their responses by understanding the mood of the conversation. By analyzing sentiments expressed in social media forums, blogs, and news articles, organizations can monitor their brand's public perception. Sentiment analysis can help understand customer preferences and dislikes based on their feedback on various platforms, and this assists in market research for new products and services. Marketers can use sentiment analysis to gauge the public reaction to their advertising campaigns. These tools can also be used for planning and implementing effective PR strategies, understanding customer grievances, identifying trends, and leveraging social media influencers. Sentiment analysis can be used to gauge customer reactions to new products or services using feedback from e-commerce sites and other platforms. Sentiment analysis is very useful in the political arena as well. Public platforms like Twitter, now X, help authorities gauge public reaction to new policies, and political parties can use this data to refine their strategies. In this course, we'll be building a deep learning model for sentiment analysis using TensorFlow and Keras. TensorFlow, as you likely already know, is a free and open source software library for machine learning and artificial intelligence. It's used for all kinds of mathematical computations, but is particularly focused on training and inference of deep neural networks, and it makes this building and training of neural networks very simple and intuitive using Keras as an API. Keras is not a deep learning framework. Instead, it's a high level neural networks API written in Python. Keras runs on top of TensorFlow and is designed to enable fast experimentation with deep neural networks, focusing on user friendliness, modularity, and extensibility. Keras makes working with TensorFlow easy.