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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How to Train and Deploy Machine Learning Models

The process of creating a Machine Learning (ML) model includes training an ML algorithm with relevant data, so the model can make predictions on similar kind of unseen data. The prediction may be a classification (assigning labels) or a regression (a real value). The goal of a machine learning project is to achieve the final model that predicts accurately.

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5 Things to Consider when Building Machine Learning Projects

The landscape of artificial intelligence and machine learning has been rapidly evolving over the past few years. Computers teaching themselves to recognize patterns and predict the future feels like a detail in almost every Ray Bradbury novel explicitly stated or not. It feels mystical, inaccessible, and far-fetched. However, machine learning technology is already part of our day-to-day lives. Though the technical aspects of machine learning technology may be impossible to grasp, the most crucial steps that managers must take to successfully introduce machine learning technology is far less technical and easy to understand. Most people know that the key to any successful project is to first define a problem statement. However, a more ML-specific rule is that all projects require a long process for preprocessing data. This is a less technical process than writing code but will impact the outcome and success of your ML project dramatically. Here are a few things to keep in mind to deliver impactful results before, during, and after creating machine learning projects:

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