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Getting Started with Sentiment Analysis using Python

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Sentiment analysis is the automated process of tagging data according to their sentiment, such as positive, negative and neutral. Sentiment analysis allows companies to analyze data at scale, detect insights and automate processes. In the past, sentiment analysis used to be limited to researchers, machine learning engineers or data scientists with experience in natural language processing. However, the AI community has built awesome tools to democratize access to machine learning in recent years. Nowadays, you can use sentiment analysis with a few lines of code and no machine learning experience at all!


How to Build a Twitter Sentiment Analysis System

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In the field of social media data analytics, one popular area of research is the sentiment analysis of twitter data. Twitter is one of the most popular social media platforms in the world, with 330 million monthly active users and 500 million tweets sent each day. By carefully analyzing the sentiment of these tweets--whether they are positive, negative, or neutral, for example--we can learn a lot about how people feel about certain topics. Understanding the sentiment of tweets is important for a variety of reasons: business marketing, politics, public behavior analysis, and information gathering are just a few examples. Sentiment analysis of twitter data can help marketers understand the customer response to product launches and marketing campaigns, and it can also help political parties understand the public response to policy changes or announcements.


Introduction 5 Different Types of Text Annotation in NLP

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Natural language processing (NLP) is one of the biggest fields of AI development. Numerous NLP solutions like chatbots, automatic speech recognition, and sentiment analysis programs can improve efficiency and productivity in various businesses around the world. 


ML.NET: Machine Learning for .NET Developers

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Microsoft released ML.NET as a commitment to making machine learning a great and easy experience in .NET. First, let's go over the basics of machine learning. Machine learning is getting computers to make predictions without being explicitly programmed. Machine learning is used to solve problems that are difficult (or impossible) to solve with rules-based programming (e.g., if statements and for loops). For instance, if you were asked to create an application that predicts whether an image has a dog in it or not, you might not know where to start. Similarly, if you were asked to write a function that predicts the price of a shirt based on the description of the shirt, you might start by looking at keywords such as "long sleeves" and "business casual," but you might not know how to build a function to scale that to a few hundred products.


ML.NET: Machine Learning for .NET Developers

#artificialintelligence

Microsoft released ML.NET as a commitment to making machine learning a great and easy experience in .NET. First, let's go over the basics of machine learning. Machine learning is getting computers to make predictions without being explicitly programmed. Machine learning is used to solve problems that are difficult (or impossible) to solve with rules-based programming (e.g., if statements and for loops). For instance, if you were asked to create an application that predicts whether an image has a dog in it or not, you might not know where to start. Similarly, if you were asked to write a function that predicts the price of a shirt based on the description of the shirt, you might start by looking at keywords such as "long sleeves" and "business casual," but you might not know how to build a function to scale that to a few hundred products.