This GitHub repository contains a Natural Language Processing (NLP) prediction model built using the Naive Bayes classification algorithm. The model is designed to analyze and classify feedback provided by individuals as either positive or negative sentiment. Sentiment analysis is a crucial component of understanding user opinions and can be applied to various applications, such as product reviews, customer support interactions, and social media sentiment tracking.
Key Features:
Utilizes the Naive Bayes algorithm for sentiment classification. Preprocesses and tokenizes textual feedback to extract meaningful features. Demonstrates text vectorization techniques for converting text data into a suitable format for machine learning. Provides training and evaluation code to assess model performance. Offers a straightforward and interpretable approach to sentiment analysis suitable for beginners and as a baseline model for more advanced NLP projects. Whether you are looking to gain insights from customer feedback or want to understand sentiment trends in textual data, this repository serves as a valuable resource for implementing sentiment analysis using the Naive Bayes classifier in Python.