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This repository contains implementations of text classification using a Rule-Based Classifier and Bag of Words model, as well as word embeddings using the Skip-gram model of Word2Vec. It includes detailed preprocessing steps, model training, and relevant references.
This repository houses a Streamlit web application for fake news detection. The app allows users to input a news article and predicts whether it is likely fake or real based on its content. It provides options to select different vectorizers (TF-IDF or Bag of Words) and classifiers (Linear SVM or Naive Bayes) to customize the prediction model.
Our team sought to perform sentiment analysis on Twitter tweets in anticipation for Hideo Kojima's video game release, Death Stranding, in 2019. We sourced the Tweets from two libraries, preprocessed them, stored them using MongoDB and then performed sentiment analysis.
A spam classifier is a software or machine learning model that categorizes incoming messages or content as either "spam" (unwanted or irrelevant) or "ham" (legitimate or relevant), using automated techniques.
Review sentiment based on drug user reviews text/ dataset, using a supervised binary text classifier, which will classify user reviews as positive or negative
In this data cleaning has been done with the help of nltk library and other library which include wordnetlimmitizer , ,and steaming of word has been donw by portersteammer ,
This repository has the implementation of traditional NLP techniques like Bag Of Words (BoW) and TF-IDF from scratch and then comparing the results with the scikit learn's respective libraries/modules vectorizers.