Overview
We are going to talk about ML Sentiment Analysis which is able to conveniently explore the aspects of Machine Learning Framework using .NET with Cross Platform Integration. Since the framework can easily be integrated with the .NET development environment the whole process explains the scalability and compatibility of the ASP.NET.
Okay let’s just mention the elephant in the room!…..What is Sentiment Analysis, How To Use ML .NET? Sentiment Analysis is basically used in machine learning for emotions of text and speech poem and it is the application creating for Example industries, including online platforms, And Also this system will be come in Robotics Machine As well, customer service, and more.sentiment analysis can be used in ASP.NET Core to analyze the sentiment of user-generated content, such as product reviews or comments on a blog post.
There is a process that we need to follow to implement sentiment analysis in ASP.NET Core.As Simple as this is the most powerful tool for analyzing content in ASP.NET Core Application.
Introduction
Hope you Know very well basics of Artificial Intelligence(AI)/Machine Learning, Let’s Understand Little Bit Advance About ML Sentiment Analysis Using .Net Core, In ASP.NET Core, Sentiment Analysis can be integrated into web applications to analyze the sentiment of user-generated content such as comment, review, poem.
This Is the process of using natural language processing and machine learning techniques to identify the sentiment of a given text,whether it is positive or negative or neutral. In Recent years,sentiment analysis has become a popular tool in many industries,such as marketing,customer service,and social media analysis.
Prerequisites
- Visual Studio 2022
- ML.NET Model Builder Extension for Visual Studio
Aspects Of ML Sentiment Analysis
There Are Basically 5 Aspects Of ML Sentiment Analysis we find easy to understand with these Aspects.
- Standard Sentiment Analysis
- Fine Grained Sentiment Analysis
- Emotion Detection
- Aspect based Sentiment Analysis
- Intent Detection
Initiation
As per the process of the ML .NET Sentiment Analysis there are 3 to 4 Steps as simple to go with console app. First of all before to create project you need to install ML.NET Model Builder
- As simple that we have to open visual studio(2022) then create one Console Application Give The Name As Per Your Project.
- After creating Console Application right click on project and go to add project and find Machine Learning Option tap.
- After the add machine learning The Model Builder Window Will Open Which Is The Process In That As We Need To Follow, Select The Data Classification Which Is Classify Tabular Data in 2+ Categories.
- Environment On Local(CPU) Then Go with as usual steps To tap on Next Step.
- After The Environment In Data That We Need To Choose One SQL Server If you have data in the database so particular select the data table in database and field Then After Give The Connection Check Your Data is this as usual which you have selected and go with the same. In this we can add csv, txt, tsv file with two columns which is for the negative and positive for the prediction.
- Now time to Train And Evaluation of your model and Data which we will see over there some of information that Training setup summary And Time to Seconds which Is You need to add seconds as per the data in the database but make sure at least 60 seconds need to add.
- Evaluating its time showing Result the input data affected terms in input text is predicting the data and showing the result, Accuracy, Model and need to try our own model as simple as that which we gave the connection. In this we can use as a set of some Limit of Data using machine learning for sentiment analysis in ASP .NET Core,needed high-quality labeled data, because of the risk of performance and scalability issues when dealing with different types of large data.
- Just As simple as we need to add Web API and Console App run the application consumes the model.
- If you want to check in console so click on Console App Add to solution And Click on the Web API Add a Solution will Consume mode.
In This a .NET Console Application That Uses Your Model To Make Prediction And For The Web API An ASP .NET Core Web API Consume Your Model.
Deploy And Use
Deploy and use your asp.Net Core application to a web server or cloud service.
There are many options available for deployment, such as Azure App Service or AWS Elastic Beanstalk.
After the successful given implementation of text that you need to finally deploy your project based on required or we modified as per the same process.
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Conclusion
Sentiment Analysis is a powerful application of machine learning that can provide valuable insights into customer behavior and opinion. When implementing sentiment analysis in ASP.NET Core, developers should carefully consider the trade-offs between accuracy, speed, scalability and choose the appropriate machine learning framework and model architecture that best suits their needs.
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