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Text Analytics
Text Analytics in Big Data Analytics
- It analyse the content of the text
- the text data is considered here which explains the freelancers profile and the rate / hour in term of US Dollars
- The concept is fixing a model which offers the skills of freelancer and needed to forecast their salary in hourly basis
- the underlying code demonstrates the way to change the raw data i.e. skills of clients in a set of text or word matrix form
- To achieve this, tm (R library) is employed
- It refers that for every word in the data , it is required to generate variable with the count of appearances of every variable
- To get a sparse solution , it is needed to fix a model to represent text as a sparse matrix
- An another substitute for this scenario is employing Least Absolute Shrinkage and Selection Operator shortly noted as LASSO
- It is a regression model which chooses the key applicable features to forecast the target
- This is model which forecasts an hourly salary of a freelancer once the collection of skills is offered
- When more data is gathered, the resultant performance of the model will increase
- The code to deploy this stream will be same
![Text Analytics text analytics img1](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/text-analytics-img1.jpg)
![Text Analytics text analytics img2](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/text-analytics-img2.jpg)
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