/ML.NET_IMDB_sentiment

This project demonstrates the use of ML.NET for creating, training, and making predictions with a sentiment analysis model. It features user-selectable IMDB datasets for flexible model training and provides real-time sentiment classification of user-inputted text.

Primary LanguageC#Creative Commons Zero v1.0 UniversalCC0-1.0

Sentiment Analysis Classifier with ML.NET and User-Selectable Datasets

This repository contains a sentiment analysis classifier built using ML.NET. The application classifies movie reviews from the IMDB dataset into positive and negative sentiments.

About

The project uses three different IMDB datasets which include text reviews and their corresponding sentiment labels (1 for positive and 0 for negative). The user can select which dataset to use at runtime. The model predicts whether a review is positive or negative based on the review's text. This type of problem is a typical binary classification task.

Features

ML.NET

The project uses ML.NET, a cross-platform, open source machine learning framework.

User-Selectable Datasets

The user can select from three different IMDB datasets at runtime.

Binary Text Classification

The project is an example of binary text classification, one of the most common applications of Natural Language Processing (NLP). ## How to Run You can clone the repository and open the solution in your preferred C# IDE. Make sure you have the .NET Core SDK installed.

Example output

The console application will prompt the user to select a dataset and enter a text for sentiment analysis. The application will then classify the sentiment of the text as positive or negative, and display the result in the console.

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Contributions

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

CC0