It looks like we’ve officially arrived in the future – AI and machine learning technology aren’t just the stuff of SciFi any longer. From our smartphones to cars, to regular customer service interactions, we use machine learning every day.
Machine learning’s capabilities, computational power, and use cases grow by the day. Some you may be aware of and some may be completely new. So, just what are the top applications of machine learning?
Sentiment analysis is commonly used for “social listening” or social media sentiment analysis to follow what customers are saying about brands and products online in real time.
Sentiment analysis or opinion mining is the process of analyzing text for “opinion polarity” (positive, neutral, negative, etc.) to automatically read it for the feeling and emotion of the writer.
It uses machine learning with Natural Language Processing (NLP) to allow machines to “understand” human language.
NLP combines the study of linguistics and data science with powerful machine learning algorithms to break down language and create programs that can understand, analyze, and extract meaning from text.
If you need a customized sentiment analysis model, which can detect specific language, sign up to MonkeyLearn and create your own sentiment analysis model in a point and click interface.
Chatbots are computer programs – usually on a website or smartphone app – that simulate human conversation by automatically responding to questions with learned information. Chatbots use NLP and NLU (Natural Language Understanding), like topic analysis and intent detection, to analyze, categorize, and “understand” queries, so that they can route them to the correct employee or department, respond to customer queries, and even make product recommendations. Chatbots have been shown to reduce customer service costs by up to 30% by automatically solving 80% of routine customer support tickets.
With the help of deep learning and convolutional neural networks that chain together multiple machine learning algorithms – to work much like the human brain does – image recognition, face detection, and image and video analysis have come a long way in recent years.
Image recognition is used by municipal governments to help keep traffic under control or by utility companies to help locate power outages. Facial detection (or facial recognition) is used by law enforcement to identify suspects or by companies to recognize employees and allow them passage into buildings. But, probably the most common face recognition system we’re all familiar with is on Facebook, when the software automatically tags friends and family in our photos.
Self-driving cars use “computer vision,” a form of image and video analysis but quite a bit more advanced – aiming to mimic actual human vision by analyzing and responding to data in real time.
Because the machine learning technology of driverless cars must react in fractions of a second, it takes many many millions of images, hours of video, and miles of road to train them. Google’s self-driving project Waymo has already launched its “robotaxi” service in the suburbs of Phoenix, where customers are able to hail a ride with no backup driver.
Text classification is a form of text analysis that includes sentiment analysis and other techniques, like topic analysis and intent detection. It’s the process of assigning “tags” or categories to text according to its content – from documents, customer service data, surveys, online reviews, and more. Models can be trained for individual business uses to automatically organize and analyze text for powerful insights.
Topic analysis put to work on Net Promoter Score (NPS) surveys, for example, can organize them by topics, like Customer Support, Ease of Use, Features, and Pricing. Once you have them organized by topic, you can perform sentiment analysis on each topic, to find out which aspects (topics) of your business are strongest and which may need some work.
This is a process called aspect-based sentiment analysis. Below is an example of the result of aspect-based sentiment analysis run on a customer comment:
I like the new update, but it seems really slow. I can’t get tech support on the phone.
Text classification allows you to run multiple analyses together to get super fine-grained results or help automate and streamline processes. Try out this pre-trained email response classifier that automatically sorts emails by topics: Interested, Not Interested, Unsubscribe, and Wrong Person, Autoresponder, or Email Bounce to save time when wading through thousands of emails.
Yet another great use of machine learning is keyword extraction (AKA keyword detection or keyword analysis), which automatically pulls the most used and most important words and phrases from a text. Keyword extraction helps summarize the content of documents, web pages, survey data, and more; find out the main topics discussed; even uncover emerging trends in the market.
Below is an example of keyword extraction used on a comment from an angry customer.
Notice how machine learning automatically joins words that go together into phrases “order number,” “slow deliver,” and “poor customer support.” Keyword extraction can be used on customer service data from internal CRM systems, surveys, Twitter, and more, to break through big data and uncover actionable insights.
Speech recognition software uses NLP to convert natural spoken human language into a format that machines can process.
Speech recognition is what runs virtual personal assistants and smart assistants that we use every day, like Alexa, Siri, and Google Assistant. Machine learning technology allows them to recognize distinctive voices and continue to learn to the needs of individual customers.
Machine learning is becoming a helpful tool for a number of financial applications and is finding a lot of success in the fight against fraud. Machine learning programs are used to scan huge sets of historical financial data to detect anomalies and automatically block transactions or notify the appropriate authority.
Internal business controls allow payment systems to recognize only authorized administrators and hold payments back until the proper approval has been initiated. Real-time learning and analysis with constant database updates can catch fraud right at the point-of-sale or right as a withdrawal request occurs, helping save businesses millions of dollars.
Eightfold AI, developers of the Talent Intelligence Platform, are one of the companies at the forefront of using AI and machine learning for Human Resources management, talent acquisition, employee development, and diversity hiring applications.
Eightfold AI promises to “unlock the hidden capabilities of your employees” and better match prospective employees with open positions, by fitting work requirements with relevant skills and removing inherent human biases that lead to a lack of diversity in hiring practices.
Machine learning language translation or, simply, machine translation (MT) has made huge strides in recent years. Advancements in artificial neural networks and the ability to store and process huge amounts of data allow machine translation to learn and access learned data much more quickly.
These advancements allow businesses to translate important documents in a trusted, cost-effective manner and give international travelers deeper access to previously confusing menu items.
OCR (optical character recognition or optical character reader) is the machine learning-aided recognition and conversion of typed, printed, or handwritten text into machine-encoded text. It uses a combination of hardware and software that scan written text onto a screen or into a database, etc. for further data analysis.
It can be used to convert written text and drawings into tables and graphs, in conjunction with machine translation to translate books in real time, or to scan physical checks and deposit them with banking apps.
MonkeyLearn is a SaaS text analysis platform that uses powerful machine learning techniques to analyze all manner of text for real-world, data-driven insights.
Perform social listening to find out what customers are saying about your brand on Twitter, Facebook, Instagram, or all over the web. Put aspect-based sentiment analysis to work on your CRM data or surveys to automatically categorize feedback by topic and find out where it’s positive and where it’s negative.
December 4th, 2020