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.
Document management is an essential part of an organization’s business operations. From contracts to invoices and forms, documents are crucial to maintaining records, addressing grievances, and conducting day-to-day operations. Over the past few decades, businesses have invested in digitization efforts to automate data capture and entry into their systems of record. The biggest drivers of this trend have been an increasing focus on data security and achieving better process efficiency.
With data at the center of modern businesses, digitization is key to leveraging that data in a meaningful way. However, despite digitization having been around for a while, paper-based manual business processes are still very much around, even in highly automated industries like manufacturing and logistics.
As we head deeper into a data-first world, decision makers rely on accurate reporting and data. Data informs how businesses respond to change, achieve their strategic objectives, and serve their customers. Given how businesses grow based on the velocity of their data, manual data processes simply can’t keep up.
Automated data capture systems allow businesses to tap into this data and bridge the gap between data and insights. In this article we’ll explore what automated data capture systems are, how they work, and some of the most common use-cases.
Automated data capture systems (ADCS) are a combination of software and hardware that mechanize the entry of information into a system of record. They have been at the center of digitization efforts and have helped businesses streamline data entry. Today, data entry processes are increasingly being run with artificial intelligence (AI) and machine learning (ML) technologies.
With data volumes exploding, manual processes are becoming more complicated and expensive. Consider this — businesses typically lose between 5-15% of all paper documents and 30% of employees’ time is spent trying to locate these documents. Add to that the fact that employees spend 35% of their time creating, storing, searching, and managing paper documents, and here’s where AI and ML come in.
ML models excel at document classification (sorting). These models are trained on data sets that feature variations in different types of documents — enabling algorithms to recognize similarities and differences that ultimately allow them to successfully classify documents at scale. ML models greatly cut down on the manual effort required in document classification.
AI models, on the other hand, are effective in extracting the contextual information from the document. ML models trained to understand context can extract crucial information like invoice number, bill amount, and vendor names regardless of whether this information is structured (digital-first documents) or unstructured (handwritten notes). Over the past few years AI algorithms have matured to the point where they can analyze different formatting and patterns to extract information even without the need for labeling.
AI’s emerging capabilities in this area are underscored by the growing demand for intelligent data capture solutions, projected to hit $3.84 billion globally by 2024. These solutions are critical across industries that rely on document management for information, intelligence, and compliance. Sectors like banking, healthcare, government, and legal services are expected to drive immediate and long-term demand for automated data capture solutions.
Organizations are examining the link between data capture and process automation to make sure that the right information is being recorded. While it’s no secret that automation excels at helping businesses scale repetitive tasks and improve employee productivity, ADCS offers a host of other benefits too.
Here are three ways ADCS enables businesses to do more with less:
Even the most seasoned data operators make mistakes. And these mistakes can quickly escalate to thousands or hundreds of thousands of dollars in missed revenue opportunities or even expensive lawsuits resulting from inaccurate data. Automated data capture systems can greatly minimize the risks associated with human errors.
AI and ML algorithms can not only help you scale data capture and extraction but also validate the correctness of the data collected. A 2009 paper by researchers at the University of Nevada and Anderson University found that automated data capture systems had an average mistake rate of 0.38 errors per 30 sheets of data versus 10.23 errors for humans against the same dataset.
AI goes a step further and validates the data extracted from a document against existing information in other systems. For instance, when processing paper invoices, intelligent data capture systems can match the vendor name against the existing data in the accounting software.
We’ve outlined in sections above how manual processes can become bottlenecks in data collection and analysis. Expert data entry operators typically type at 15,000 keystrokes per hour. However, that speed can come down dramatically when operators deal with complex data that requires comprehension before entry. So, an expert operator entering 400 units of data would take anywhere between 6-8 minutes.
What happens when the data volume is higher? The turnaround time increases proportionally.
ADCS can scale data capture seamlessly because unlike human operators, these systems don’t need to examine every single version of a document to extract relevant information. Remember how we mentioned ML’s applications in data classification? ML models combined with robotic process automation (RPA) concepts can be used to create AI engines that employ a dynamic variance network that allows an ADCS to compare every component of a document with each other.
In other words, the software calculates all vectors between characters in the document against the target fields to promote data capture at scale.
While common sense would dictate that costs associated with manual data processes are primarily labor-related, the truth is that the real cost of manual data entry is the error of correction. Errors have a more damaging impact on the bottom line than the actual cost of labor. Incorrect data costs businesses at least 30% or more of their revenue. Manual data verification and error correction costs are often overlooked.
A Goldman Sachs report found that the direct and indirect costs of manual, paper-based invoicing costs businesses $2.7 trillion annually.
Automation is not only more cost effective but also helps businesses realize significant improvements in customer satisfaction, brand perception, and employee satisfaction. Additionally, ADCS systems also enable organizations to improve their document management practices, resulting in better efficiency across all departments.
The workflow for a typical automatic data capture processes involves five major steps:
While technologies like optical mark recognition (OMRs) and barcode scanners have been around for over five decades now, AI- and ML-based solutions are driving the modern shift to automated data collection. Let’s look at some examples of automated data capture systems in use today.
OCR is perhaps the most commonly found functionality for data capture automation today. OCR algorithms are able to convert a photo of a document into a fully editable digital file. It offers near instant document digitization capability for businesses. With OCRs, businesses can process, manage, store, and share the most important data without human intervention.
OCR algorithms have been adapted for multiple business use-cases for printed text. The technology is relatively simple and highly scalable — two reasons why it has found widespread adoption across businesses of all sizes.
IDC is an evolution of OCR technology and combines image recognition capabilities with data interpretation, which allows businesses to gather more meaningful insights from data.
An example of IDC you’re likely to have come across is Google Lens. It will not only tell you what the image or photo is but also provide meaningful content from it. For instance, if you scan a bill using IDC, you’ll not only get useful filing information like the document data but also additional context such as bill due date.
IDCs rely on deep learning or neural networks that have been trained on large sets of annotated data. IDC leverages smart parsing to structure data from images or printed text. This results in massive productivity gains as businesses no longer need as many humans in the loop.
If you’ve ever used an intelligent virtual assistant like Siri or Alexa, you’ve experienced the power of voice recognition technology. The ease-of-use and mobile availability of voice search has resulted in rapid adoption of voice recognition technology. It’s estimated that in 2020, half of all searches were performed using voice, and the trend is only growing.
Voice recognition also has massive applications in the world of business — auto transcription and note-taking are some of the most popular use-cases for voice recognition software. The software is powered by deep learning models that have been trained with large data sets around vocal patterns and speech elements. Labelling or annotating big data for context is also an important step towards developing voice-recognition software.
While facial recognition software is ubiquitous across consumer technologies like handheld mobile devices, it also has numerous applications in business. At its core, facial recognition software leverages image recognition algorithms to detect, capture, and match a user’s face against a database.
Facial recognition data collection enables software to collect biometric information like the spacing of eyes, bridge of the nose, contour of the lips, ears, and chin. Automated systems then use this information to authenticate and identify people.
What makes facial recognition software so important is that it finds application in day-to-day consumer technologies such as the iPhone, as well as business and law enforcement biometrics. It can be used in dynamic and unstable environments (like a crowded airport). Emerging business applications of facial recognition include security/verification, match databases, biometric ID, and sentiment analytics.
Manual data entry is no longer efficient. Paper workflows are damaging for businesses as valuable time is lost in creating, converting, and safely storing data. Automated data collection addresses productivity and cost concerns around document management. Businesses capturing data with automated methods have realized multiple benefits such as decreased security risks and higher customer satisfaction scores, ultimately leading to revenue growth.
However, building automated data capture systems is far from easy, particularly when designing custom solutions for specific business needs.
If you’re looking to leverage data collection to reduce human error, get accurate insights, and improve your staff’s productivity, let’s talk!
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.
Accelerate your growth by pivoting key areas of your business to AI. Your business outcomes will be achieved quicker & you’ll see benefits you didn’t plan for.
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.
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