Popular GitHub Repositories for Tfidf Vectorizer

Developer friendly Natural Language Processing ?

使用python抓取微博数据并对微博文本分析和可视化，LDA（树图）、关系图、词云、时间趋势（折线图）、热度地图、词典情感分析（饼图和3D柱状图）、词向量神经网络情感分析、tfidf聚类、词向量聚类、关键词提取、文本相似度分析等

RETVec is an efficient, multilingual, and adversarially-robust text vectorizer.

结合python一起学习自然语言处理 (nlp): 语言模型、HMM、PCFG、Word2vec、完形填空式阅读理解任务、朴素贝叶斯分类器、TFIDF、PCA、SVD

Text vectorization tool to outperform TFIDF for classification tasks

This repository is for my students of Udemy. You can find all lecture codes along with mentioned files for reading in here. So, feel free to clone it and if you have any problem just raise a question.

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Analisis Sentimen Twitter dengan TFIDF-ANN

BERT, LDA, and TFIDF based keyword extraction in Python

[SOICT 2024] LLM-Powered Video Search: A Comprehensive Multimedia Retrieval System

Document Search Engine project with TF-IDF abd Google universal sentence encoder model

This project employs emotion detection in textual data, specifically trained on Twitter data comprising tweets labeled with corresponding emotions. It seamlessly takes text inputs and provides the most fitting emotion assigned to it.

NLP tutorial

Movie Recommendation Chatbot provides information about a movie like plot, genre, revenue, budget, imdb rating, imdb links, etc. The model was trained with Kaggle’s movies metadata dataset. To give a recommendation of similar movies, Cosine Similarity and TFID vectorizer were used. Slack API was used to provide a Front End for the chatbot. IBM Watson was used to link the Python code for Natural Language Processing with the front end hosted on Slack API. Libraries like nltk, sklearn, pandas and nlp were used to perform Natural Language Processing and cater to user queries and responses.

Here I sort out some small projects I did in the process of learning NLP.

NLP based Classification Model that predicts a person's personality type as one of the 16 Myers Briggs personality types. Extremely challenging project dealing with correlation between human psychology and casual writing styles and handling heavily imbalanced classes. Check the app here - https://mb-predictor-motetuzs5q-uc.a.run.app/

A web app that classifies text as a spam or ham. I am using my own ML algorithm in the backend, Code to that can be found under machine_learning_section. For Live Demo: Checkout this link

The project is based on a multi-label classification problem in NLP.

A sentiment analysis classifier for short texts in Python

Developed BERT, LSTM, TFIDF, and Word2Vec models to analyze social media data, extracting service aspects and sentiments from a custom dataset. Provided actionable insights to telecom operators for customer satisfaction and competitive analysis.