Solve your natural language processing problems with smart deep neural networks

Predicting Political Ideology of Twitter Users.

This notebook contains entire text preprocessing pipeline for NLP problems. The ready-to-use functions require NLTK and SKlearn package installations. It also contains some prominent text classification models.

A basic machine learning model built in python jupyter notebook to classify whether a set of tweets into two categories: racist/sexist non-racist/sexist.

Turkcell&Miuul Data Science Bootcamp - Assignments

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Sentiment analysis on BBC News headlines and descriptions using VADER. Classifies text as Positive, Negative, or Neutral based on compound scores.

Unsupervised Machine Learning project for Netflix Movies and TV Shows Clustering. The main goal of this project is to create a content-based recommender system that recommends top 10 shows to users based on their viewing history.

All NLP related courses on DataCamp

Hey there! Welcome to NLP project repository. Here i will upload all the NLP based projects i have done or i am currently doing. Feel free to fork this repo and contribute in it.

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For the text Mining course I carried out a project related to the analysis and classification of the reviews of the "UCI ML Drug Review" dataset (link: https://archive.ics.uci.edu/ml/datasets/Drug+Review+Dataset+%28Drugs.com%29). I learned to apply techniques such as bag of words, TF-IDF and build sentiment analysis models through the Bert and Vader model.

End-to-end implementations of Natural Language Processing (NLP) projects and experiments, featuring sentiment analysis, text preprocessing, and machine learning models.

AI or Human! Detection of AI Generated Text

Implemented Text Summarization by using Text Ranking(simple graph based technique) and Sq2Sq Encoder Decoder Model

LangChainRunnables is a Python-based project showcasing and learing five distinct LangChain workflows—Branch, Lambda, Parallel, Passthrough, and Sequence—using OpenRouter’s free API. It demonstrates AIdriven text processing tasks generating facts, summarizing reports, creating notes and quizzes, responding to sentiment feedback, and crafting jokes

Machine learning model to predict emotions throught text

Performed PySpark based text pre-processing including lemmatization, POS tagging and UDF functions on customer feedback. Computed and visualized sentiment score to identify areas of improvements.

A novel approach towards video-ranking using intent and relevance feedback

NLP starter kit

Fuzzy Matcher utility provides you robust fuzzy matching based on Levenstein distance enabled with caching and parallization

Twitter-Sentiment-Analysis-Chandigarh University

AlmaBetter Capstone Project -Classification model to predict the sentiment of COVID-19 tweets. The tweets have been pulled from Twitter and manual tagging has been done then.

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NLP using NLTK python library

This notebook contains entire text preprocessing pipeline for NLP problems. The ready-to-use functions require NLTK and SKlearn package installations. It also contains some prominent text classification models.

Instance of CBOW(Continuous Bag Of Words)-bigram model

Here we will apply natural language process using variety packages like keras, textblob, genism etc

IndoBERT is used for sentiment analysis of product reviews, helping businesses understand customer opinions. With fine-tuning, the model improves sentiment classification accuracy, enabling more effective marketing strategies such as ad personalisation, quick response, and service improvement based on customer feedback.

SpamGuard is an intelligent SMS filtering system designed to detect and filter spam messages using machine learning techniques.