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

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.

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

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

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

This project is to perform some tasks that are commonly used in Natural Language Processing. This includes design of the shiny app to analyze the Toyota Camry car reviews. The reviews are available online and it will be programmatically downloaded and their sentiment score will be predicted.

NLP Projects for Data science

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

Tokenization, Stemming, Lemmatization, Bag of words, TF-IDF

🍰 A library for creating n-grams, skip-grams, bag of words, bag of n-grams, bag of skip-grams.

Project it is implement a model for a simple sentiment analysis, using Spark, HDFS and Scala/Java

Sentiment analysis of tweets to detect negative tweets.