Recent Posts by Bikash Sharma

Top 5 Sentiment Analysis Example

The term sentiment analysis is quite popular. But most people do not really notice how important it is for a company’s success. Sentiment analysis provides an insight into all the problems that your company may be facing. It is like being able to listen to all the conversations that your customers are having about your product. It helps you understand how your brand is being perceived and work towards improving its image based on those insights.

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Understanding Image Recognition and Its Uses cases

Vision is easy for human brains. We can easily tell apart a tree, a bus, a dog, or an airplane. But its not the same with computers. It takes a lot of effort and skill to teach machines to ‘see’ like us. But humans have been successful in doing so. We have enabled machines with our natural skills to see, learn by example, and understand the world. With the help of computer vision and image recognition, we have taught machines to perform tasks involving vision. And when this capability is brought into businesses with a practical purpose, all this effort is worth it. 

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How to Build a Twitter Sentiment Analysis Tool using Skyl.ai

Hundreds of millions of people willingly spew their opinions in under 280 characters per post and 6,000 times per second. Sentiment analysis on social media platforms such as Twitter is a very effective way for analysts to gauge consumer reactions to products and services. The use of machine learning is necessary to properly gather consumer reactions. Individuals sifting through tweets ranging from “Where the Buffalo Wild Things Are” to firey, researched political statements can easily get in the noise and twitter void, especially if a business just wants to gauge the attitude towards a particular product or service.

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Learn about state of art Sentiment Analysis Algorithms

Introduction to Sentiment Analysis

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Why Is Content Moderation Important for an Online Business?

With increasing competition, online businesses are finding it hard to thrive. They need more creative marketing techniques to attract consumers. User-generated content is one such technique that is becoming the most powerful marketing tool used by online businesses today.  

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How Does Content Moderation Using AI work?

With the advent of the internet, it has never been easier than it is now to publish content. Anyone with a smart device and a web-connection can publish on a multitude of platforms catered for that specific purpose. In recent years, a global debate has emerged around the risks faced by internet users, with a specific focus on protecting users from harmful content. A key element of this debate has centered on the role of content moderation to protect users from potentially harmful material. 

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Understanding Deep Learning Algorithms for Named Entity Recognition: Skyl.ai

What is Named Entity Recognition (NER)

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Build a Named Entity Recognition (NER) ML model in hours

Named-entity recognition (NER) also known as entity extraction is a subtask of information extraction that aims to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, places, expressions of times, quantities, monetary values and more.

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What is Topic Modeling? An Introduction to Topic Modeling Use Cases

Imagine that you need to find a specific piece of information for your company, from a large collection of documents, within a tight deadline. It may be an important statistic or a crucial piece of information needed urgently. How much time do you think it will take for you to find that information from your company database? Looking for information within millions of documents will be like looking for a needle in a haystack. It will take a long time and the task will be practically impossible. This is where Machine learning and Topic modeling comes into place.

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When Do You Need AI Consulting?

There’s a lot of buzz about artificial intelligence (AI) and the many benefits it offers to the organizations that use it. Most recently, AI has been leveraged in the healthcare industry in response to the global pandemic for mapping, to accelerate diagnoses, and to predict health outcomes. Although the technology is not brand-new (it has been around since the 1950s), the concept of AI still feels inaccessible to many.

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