Using NLP or prediction of stack overflow posts using linear models for multi-class classification

Oasis Infobyte Internship Data Science task-4

Predict whether a stock price will increase based on headlines on a specific day. Data is Wrangled and Merged for modeling. The bag of words approach is used to vectorize textual data. A combination of NLP and ML models like RanfomForestClassifier is used to predict final results, plus the Naive Bayes approach with NLP to predict the results.

Review sentiment based on drug user reviews text/ dataset, using a supervised binary text classifier, which will classify user reviews as positive or negative

Trained and optimized a Classification Machine Learning model to predict the grammatical flow of email using state of the art techniques : 1. Word2Vec 2. tf-idf 3. bag-of-words. The models used include Logistic Regression and Support Vector Mechanics with 250-300 features.

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.

Classify comments on categories - "Toxic", "Severe Toxic", "Obscene", "Threat", "Insult", "Identity Hate", "Any of the Above", "None of the Above".

I trained a natural language processing (NLP) model to classify restaurant reviews as either positive or negative sentiment.

Clustering news documents using bag of words model to classify documents

Performed Twitter Sentiment Analysis on 10 years of Twitter Data using bag of words approach, and predicted stock market trends using Time Series Neural Networks.

DNA CLASSIFICATION

Bag of Words on Text to Detect Stress

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.

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.

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.

Text Encoding and Classification using different types of Encoders

Website that helps recommend movies

This repo has bag of words, string match & fingerprinting algorithms written in Java language.