Building A GPT-3 Twitter Sentiment Analysis Product
We’re going to walk through building a production level twitter sentiment analysis classifier using GPT-3 with the popular tweet dataset Sentiment140.
Artificial intelligence is not just the future of your business — it's a technology you should implement as soon as possible to increase your competitive advantage, profitability, customer satisfaction, and almost all business outcomes. If you're wondering how artificial intelligence can accelerate your business growth, this article is for you. You will find many practical examples that you can use to improve your business results.
The importance of AI is growing and every entrepreneur knows that digital transformation is inevitable. PwC predicts that AI will cause the GDP to increase by up to 14.5% by 2030. The same research shows that AI could contribute as much as $15.7 trillion to the global economy by then. This increasing impact of AI is caused by many business drivers such as:
While this last factor may seem like nonsense, there are indications that AI will increase the number of jobs. According to the World Economic Forum, almost 85 million jobs around the world will be replaced by AI by the year 2025. However, this new technology will help to create around 97 million new ones.
More and more companies are deciding to implement AI to help them meet their business objectives. In fact, half of global business leaders said that their companies have already adopted AI in at least one business function. If your business isn't one of them yet, let's move on to specific solutions you can implement in order to improve your business outcome.
There are a ton of ways AI can improve a company's business results. It all depends on the type of business, the size of the company, and the organization's work culture. But no matter where you apply AI, if you do it right, you will see results in a short period. Here are some examples of how AI can help your business.
Let's start with something you've probably heard before. AI, especially machine learning-based systems, allows you to automate the most repetitive, time-consuming, and tedious tasks. Need some specifics?
One example would be a case study of the online retailer B.B.C. Emporium. The company needed to find a solution that helped them streamline the listing and managing of new merchandise. They wanted to automate most of the process to spend time growing inventory.
To help them, we built advanced software based on machine learning and computer vision using both image and text recognition. Our solution automates every step of the product listing process. It can auto-edit images, scan barcodes, or categorize product types. As a result, the software reduced the workload by 22%. The AI-powered system enables their employees to do more ambitious and exciting tasks and spend more time improving the business.
This type of solution can not only save you a lot of time, but it can also make your employees more engaged and happy at work. Their work will no longer be boring and monotonous, which can help improve employee retention and reduce turnover in the company.
Did you know that Netflix's recommendation engine is worth $1 billion per year? According to the Netflix team, "consumer research suggests that a typical Netflix member loses interest after perhaps 60 to 90 seconds of choosing, having reviewed 10 to 20 titles on one or two screens. The user either finds something of interest or the risk of the user abandoning our service increases substantially.” Netflix executives think they could lose at least $1 billion yearly if their subscribers don't get a proper recommendation.
Netflix's case is just one of many examples that shows the importance of a recommendation system on business outcomes.
Without recommender systems, the e-commerce industry practically wouldn’t exist today. So let’s talk about another giant: Amazon. Did you know that hyper-personalized product recommendations account for as much as 35% of the company revenue?
If you've browsed their site, you may have noticed the different ways they recommend products:
These recommendations and others are based on a database analysis of your browsing history, other users' preferences, product information, buying patterns, and much more.
Another exciting way to recommend products is through personalized email marketing campaigns based on users' buying patterns interested in a similar product. Such messages sent by Amazon are usually based on a person's browsing history and recommend a list of similar products that you may be interested in. For example, if you've recently been browsing for a MacBook, you may receive a list of Apple's most popular laptops in the first email. In the second email, Amazon will send you recommendations of popular third-party laptops that you might like.
These strategies significantly improve the marketing team's key performance indicators as well as overall sales. To get more compelling examples of how recommendation systems and other AI tools are changing the e-commerce industry, read our e-book: "Breakthrough AI software tools that will increase your ecommerce company’s revenue instantly."
Artificial intelligence can extract business value from data. More than half (54%) of company executives admit that artificial intelligence has a significant impact in improving the decision-making process.
By analyzing data on customer behavior or market trends, you can make more informed and data-driven business decisions based on facts, not hunches. Surely you've heard about predictive analytics. It's a significant technology that allows you to plan your marketing or sales roadmap and set KPIs or business metrics. Based on advanced statistical algorithms, millions of pieces of relevant information are analyzed in real-time to help you make the best decision for your business.
For example, an AI-driven system can be integrated into a CRM, enabling sales teams to automatically generate valuable information, such as which customers will bring the company the most profit and which are most likely to churn. Armed with this knowledge, salespeople can focus their time and energy where it matters most.
Moreover, AI-driven decisions can be made automatically by the system and, thus, support the employees’ work. A great example of this is using predictive analytics in the retail industry. The global leaders in this sector implement AI in crucial parts of the value chain, and regional companies follow them. The benefits include marked improvements in forecasting, inventory management, and efficiency. Companies can use AI to predict trends, optimize logistics operations, set pricing, and offer personalized promotions. Some of them can even forecast customer needs, sending their products without waiting for order confirmation. All stakeholders can benefit from this solution.
Computer vision is one of the fastest-growing elements of AI. A very common application of this technology is face recognition in smartphones or face filters used by social networking applications. What business outcome improvements can computer vision bring to your company? For starters, computer vision can help you sort products or categorize documents. It can save time by sorting through invoices or other documents — even collected in paper form. But we'd like to focus on a less obvious use of computer vision: helping you understand customer needs and improve customer engagement.
Computer vision can provide customer emotion analysis. An interesting example is a study conducted by researchers from Ibn Tofail University. They used a facial expression classification that identifies various smiles, frowns, eyebrow raises, and more to predict the decision-making by customers. They used a database that contains 213 images of facial expressions of 10 Japanese female models.
The results of their study show that this information can predict the customer’s preferences for products. According to the researchers, this technology can be applied, for example, in virtual stores. They mentioned that a company used eye gaze detection to find their customers' point of gaze and then detect which products draw their attention.
At first, they collected data from a video camera. Afterward, the system processed the data of face and emotions detection. Finally, it analyzed and found emotions that generate business values.
What does it mean for your business? It means that computer vision can help marketers determine the needs and satisfaction of consumers with face expression analysis. It can optimize the effectiveness of advertising campaigns and achieve desired business outcomes. According to the researchers, the key value for the enterprise can be extracting business intelligence about how consumers perceive the product.
Using computer vision in this way is rather innovative. However, considering the dynamic pace of development of this field, we can be sure that it will already be widely used in a few years.
There are plenty of ways AI can improve your business. Choose the best one for your needs and enjoy the benefits of this fantastic technology. Schedule a consultation with our AI experts and find the solution that will help you achieve your desired business outcomes.
We’re going to walk through building a production level twitter sentiment analysis classifier using GPT-3 with the popular tweet dataset Sentiment140.
Find out how machine learning in medical imaging is transforming the healthcare world and making it more efficient with three use cases.
Discover ways that machine learning in health care informatics has become indispensable. Review the results of two case studies and consider two key challenges.
We built a GPT-3 based software solution to automate raw data processing and data classification. Our model handles keyword extraction, named entity recognition, text classification | Case Study
We built a custom GPT-3 pipeline for key topic extraction for an asset management company that can be used across the financial domain | Case Study
How you can use GPT-3 to create higher order product categorization and product tagging from your ecommerce listings, and how you can create a powerful product taxonomy system with ai.
5 ways you can use product matching software in ecommerce to create real value that raises your sales metrics and improves your workflow operations.
Data mining and machine learning in cybersecurity enable businesses to ensure an acceptable level of data security 24/7 in highly dynamic IT environments. Learn how data security is getting increasingly automated.
Product recognition software has tremendous potential to improve your profits and slash your costs in your retail business. Find out just how useful it is.
Big data has evolved from hype to a crucial part of scaling your organization in every modern industry. Learn more about how big data is transforming organizations and providing business impacts.
Learn how natural language processing can benefit everybody involved in education from individual students and teachers to entire universities and mass testing agencies.
Here’s how automated data capture systems can benefit your business in some key ways and some real-life examples of what it looks like in practice.
Use these power ai and machine learning tools to create business intelligence in your marketing that pushes your business understanding and analytics past your competition.
We built a custom ML pipeline to automate information extraction and fine tuned it for the legal document domain.
In this practical guide, you'll get to know the principles, architectures, and technologies used for building a data lake implementation.
Find out how machine learning in biology is accelerating research and innovation in the areas of cancer treatment, medical devices, and more.
An enterprise data warehouse (EDW) is a repository of big data for an enterprise. It’s almost exclusive to business and houses a very specific type of data.
Save yourself the hassle of manually importing and processing data with intelligent document processing. Learn all the details of how it works here.
Dlib is a versatile and well-diffused facial recognition library, with perhaps an ideal balance of resource usage, accuracy and latency, suited for real-time face recognition in mobile app development. It's becoming a common and possibly even essential library in the facial recognition landscape, and, even in the face of more recent contenders, is a strong candidate for your computer vision and facial recognition or detection framework.
Learn how to utilize machine learning to get a higher customer retention rate with this step-by-step guide to a churn prediction model.
Machine learning algorithms are helping the oil and gas industry cut costs and improve efficiency. We'll show you how.
We’ll show you the difference between machine learning vs. data mining so you know how to implement them in your organization.
Here’s why you should use deep learning algorithms in your business, along with some real-world examples to help you see the potential.
Beam search is an algorithm used in many NLP and speech recognition models as a final decision making layer to choose the best output given target variables like maximum probability or next output character.
Best Place For was looking for an image recognition based software solution that could be used to detect and identify different food dishes, drinks, and menu items in images sourced from blogs and Instagram. The images would be pulled from restaurant locations on Instagram and different menu items would be identified in the images. This software solution has to be able to handle high and low quality images and still perform at the highest production level, while accounting for runtime as well as accuracy.
Deep learning recommendation system architectures make use of multiple simpler approaches in order to remediate the shortcomings of any single approach to extracting, transforming and vectorizing a large corpus of data into a useful recommendation for an end user.
GPT-3 is one of the most versatile and transformative components that you can include in your framework, application or service. However, sensational headlines have obscured its wide range of capabilities since its launch. Let’s take a look at the ways that companies and researchers are achieving real-world results with GPT-3, and examine the untapped potential of this 'celebrity AI'.
Let's take a look at how you can use spaCy, a state of the art natural language processing tool, to build custom software tools for your business that increase ROI and give you data insights your competitors wish they had.
The landscape for AI in ecommerce has changed a lot recently. Some of the most popular products and approaches have been compromised or undermined in a very short time by a new global impetus for privacy reform, and by the way that the COVID-19 pandemic has transformed the nature of retail.
Extremely High ROI Computer Vision Applications Examples Across Different Industries
Building Data Capture Services To Collect High ROI Business Data With Machine Learning and AI
Software packages and Inventory Data tools that you definitely need for all automated warehouse solutions
Inventory automation with computer vision - how to use computer vision in online retail to automate backend inventory processes