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
Sentiment analysis on BBC News headlines and descriptions using VADER. Classifies text as Positive, Negative, or Neutral based on compound scores.
A structured repository covering Natural Language Processing fundamentals, text preprocessing, feature extraction techniques, and hands-on NLP implementations using Python.
All NLP related courses on DataCamp
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.
AI or Human! Detection of AI Generated Text
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.
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.
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 V…
A lightweight Python library that cleans messy scraped data (URLs, emojis, symbols, noise) and prepares it for NLP tasks like sentiment analysis.
Fuzzy Matcher utility provides you robust fuzzy matching based on Levenstein distance enabled with caching and parallization
A novel approach towards video-ranking using intent and relevance feedback
Implemented Text Summarization by using Text Ranking(simple graph based technique) and Sq2Sq Encoder Decoder Model
Complete NLP roadmap for beginners — 5 Google Colab notebooks covering Text Preprocessing, BoW, TF-IDF, Word2Vec, LSTM, and BERT. Every line of code explained. CampusX playlist companion.
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