The NLP-based reviews Classification model is a machine learning project that uses natural language processing techniques to classify review into 2 categories. The system is built using Python, TensorFlow-Keras, and NLTK libraries. The system is hosted on a local server that accepts input resumes in PDF form using a Flask API.
Dataset: Amazon Reviews
The dataset used for trainingthe model was picked from kaggle and later some was scrapped using selenium aand other web scrapping softwares.
Classifies resumes into 2 categories(Postive or Negative) based on job position and qualifications. Accepts input resumes in PDF form using a Flask API. Uses TensorFlow-Keras and NLTK libraries for natural language processing and machine learning. Hosted on a local server, making it easily accessible to the user.
This project is built using Python and several popular libraries, including TensorFlow-Keras and NLTK. The system utilizes deep learning algorithms to analyze reviews and classify them into positive or neagtive category. The system also preprocesses resumes using NLTK to remove stop words, perform stemming, and tokenization.
To use the system, users simply paste the review text to the Flask API hosted on the local server. The system then uses a pre-trained TensorFlow-Keras model to classify them as either a postive or a negative review. The user can then view the classification result on the website.
This project is an innovative solution for recruiters and HR departments looking to streamline the resume classification process. By using natural language processing and deep learning algorithms, the system can quickly and accurately classify reviews into potivite and neagtive category, helping the user in deciding wheather the product is worth it or not.