Top 8 Natural Language Processing Techniques

Artificial intelligence (AI) encompasses multiple types of machine learning approaches, including computer vision, natural language processing (NLP), audio intelligence, and so on. As the name suggests, NLP is the branch of AI that centers on language and teaching computers how to understand and use it in a natural, human-like manner.

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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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Twitter Sentiment Analysis Using Machine Learning

Natural Language Processing (NLP) is a popular technology for research and data collection, and sentiment analysis is one of the most common sub-fields of NLP. Sentiment Analysis is the process of analyzing online pieces of writing to predict their emotional tone, i.e. whether a piece of information is positive, negative, or neutral. Tweets on specific topics can be analyzed this way to understand their sentiments.

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Structured vs. Unstructured Data: What’s the Difference?

Businesses run on data. Data is so integrated into our daily lives that, most of the time, we don’t even realize just how often we use it to perform basic tasks and make decisions. Some of the ways we use data on a regular basis include:

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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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Data Annotation tool for labeling topic modeling dataset: Skyl Labelwise

Topic modeling is an unsupervised machine learning technique that's capable of scanning a set of documents, detecting word and phrase patterns within them, and automatically clustering word groups and similar expressions that best characterize a set of documents.

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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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