A no-code text analytics technology called MonkeyLearn enables companies to instantly evaluate and visualise client input. MonkeyLearn enables users to quickly gain insights from their text data and take practical steps to improve their products, customer satisfaction, and customer experience. It does this by providing pre-built and customised machine learning models, business templates tailored for different scenarios, and other features. Pricing Model: Paid You can try out this tool for free! Save my name, email, and website in this browser for the next time I comment.

Excel is perhaps the most well known data analysis tool out there. It's used to store and organize data such as sales numbers, profit rates, expenditures or revenues. Some businesses even use it to store text data. However, Excel is unable to organize text data without the help of machine learning. Machine learning algorithms can automatically analyze hundreds and thousands of rows of text data in a fast, consistent and scalable way. In other words, machine learning algorithms are able to quantify words and phrases in Excel, by assigning topics, keywords, entities, and even sentiment to each row of text.

A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. You're refining your training data, and maybe you've even tried stuff out using Naive Bayes. But now you're feeling confident in your dataset, and want to take it one step further. Enter Support Vector Machines (SVM): a fast and dependable classification algorithm that performs very well with a limited amount of data to analyze.

A few years back, startups focusing on artificial intelligence had a whiff of bullshit about them; venture capitalists became inured to young tech companies claiming that their new AI-powered product was going to change the world as hype exceeded product reality. But in the time since, AI-powered startups have matured into real companies, with investors stepping up to fund their growth. In niches, from medical imaging, of course, to speech recognition, machine learning and deep learning and neural nets and everything else that one might scoop into the AI bucket has seemed to have grown neatly in recent quarters. Indeed, AI investing has become so popular amongst VCs that this publication wound up debating the finer points of AI-focused startup revenue quality earlier this year. But AI is not the only startup niche appearing to enjoy tailwinds lately.

For some, it enhances customer experiences; for others, it reduces costs through automation. We've already seen examples of AI powering content processing and analysis, and it's set to become a crucial part of the content generation process. My company, Contentstack, was one of the firstheadless content management systems (CMS) to embed AI into its editor experience. Turnkey integrations with IBM Watson, Salesforce Einstein and MonkeyLearn have allowed our customers to leverage AI to create highly personalized digital experiences that go beyond standard demographics and traditional audience segmentation. Here are just a few ways AI has already begun to impact today's content management systems: Text intelligence and analysis: AI can already analyze the tone and sentiment of content and suggest if it is suitable for the intended audience.IBM Watsonand MonkeyLearn, for example, have developed intelligent systems that leverage natural language processing (NLP) to provide text intelligence services such as language detection, keyword extraction, profanity detection, news categorization, sentiment analysis and so on.

Communication is an integral part of businesses, not only internally, but also externally, in how they communicate with the customers and partners. Consequently, it's essential to work with a communication system in place to achieve this successfully. Having the correct communication system will consequently create effective communication between employees, clients, and stakeholders, improving customer service and as a result, customer engagement. However, with time and growth of the business comes new challenges. Customer queries start piling up and even having a successful communication system sometimes is not enough to manage the new flood of enquiries.

In part 1, I explained the voicemail capabilities of the RingCentral cloud communications system and AI solutions that can be employed to build an effective virtual voicemail assistant for your telephone customer services. I also showed you how to create and set up a dedicated extension for taking only voicemail messages and the overall workflow of a virtual voicemail assistant. In this article, I will walk through the essential steps to develop a web app -- a demo of virtual voicemail assistant for RingCentral Developers support, which can listen for new voicemail messages and perform the following tasks. The associated demo application is built using the Node JS Express Web application framework. Thus, for conveniences, I will use the Node JS SDKs provided by RingCentral, Monkey Learn, and Rev AI to access their services.

Hello everybody We're excited to share our **classification feature**; we've been working on it for a while now and iterating on it based on feedback from our customers. These are the highlights: **Active learning** it minimizes the effort while tagging and training models. That means we had to build an extremely reliable service that can tackle high volume transactions in real time. I wanted to share some of the **top 3 most frequent use cases** we've seen so far. We're amazed by how business teams are leveraging our AI technology in their operations without the need of technical skills!

MonkeyLearn is one of the most innovative and compelling platforms I've used. I've also loved working with MonkeyLearn's team - their willingness to help me build great products to help our community have put them among my favorite new companies.

Sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. In a world where we generate 2.5 quintillion bytes of data every day, sentiment analysis has become a key tool for making sense of that data. This has allowed companies to get key insights and automate all kind of processes. But… How does it work? What are the different approaches? What are its caveats and limitations? How can you use sentiment analysis in your business? Below, you'll find the answers to these questions and everything you need to know about sentiment analysis. No matter if you are an experienced data scientist a coder, a marketer, a product analyst, or if you're just getting started, this comprehensive guide is for you. How Does Sentiment Analysis Work? Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. Currently, sentiment analysis is a topic of great interest and development since it has many practical applications. Since publicly and privately available information over Internet is constantly growing, a large number of texts expressing opinions are available in review sites, forums, blogs, and social media. With the help of sentiment analysis systems, this unstructured information could be automatically transformed into structured data of public opinions about products, services, brands, politics, or any topic that people can express opinions about. This data can be very useful for commercial applications like marketing analysis, public relations, product reviews, net promoter scoring, product feedback, and customer service. Before going into further details, let's first give a definition of opinion. Text information can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about something. Opinions are usually subjective expressions that describe people's sentiments, appraisals, and feelings toward a subject or topic. In an opinion, the entity the text talks about can be an object, its components, its aspects, its attributes, or its features.