GitHub topics: tf-idf-vectorizer

Repositories

rng190001/CS6375-ResearchProject

Visual Language Model focusing on testing different parsing techniques from generated responses

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ashithapallath/Abusive_Comment_Detection_Malayalam

This project detects abusive and non-abusive comments in Malayalm Language using the MuRIL Bert model and compares its performance with TF-IDF + SVM and XGBoost. MuRIL outperforms classical models.

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codewithshek/AI-powered-Resume-Screening-and-Ranking-System

This project is all about making your hiring process a breeze. We've harnessed the power of AI to automatically screen and rank resumes based on job descriptions. Say goodbye to endless hours of manual sorting and hello to efficient, effective hiring decisions.

Language: Python - Size: 552 KB - Last synced at: 29 days ago - Pushed at: 29 days ago - Stars: 0 - Forks: 0

temulenbd/text-representation-comparison-job-recommender

PROJECT NAME: A comparative evaluation of text representation techniques for content-based job recommendation system

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zjohn77/retrieval

Tunable full text search engine in JavaScript that: (1) works natively on web apps like Express.js; (2) easy to customize (via BM25) to specific types of documents (e.g. tweets, scientifc journals); (3) is deployable on either the client-side or the server side.

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armahdavi/nlp_document_tracking_construction_management

Summary of NLP work to automate construction management for non-compliance, punch list, and database creation.

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quantum1208/Email_spam_classifier

A spam detection model using Logistic Regression and TF-IDF (Term Frequency-Inverse Document Frequency) for text feature extraction. It processes a dataset of emails and classifies them as either spam (0) or ham (1).

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chan9an/E-commerce-recommendation-system

Designed for Kaggle competitions, this project uses K-Means clustering and TF-IDF vectorization to group similar product descriptions and recommend related items. Ideal for text-based product categorization and recommendation systems.

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jalajthanaki/Basic_Ecommerce_Recomendation_System

This repository contains the code for basic kind of E-commerce recommendation engine. By using the concept of TF-IDF and cosine similarity, we have built this recommendation engine.

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RohithgowdaM/cyberbullying-classification

The project deals with the identification of high accuracy model among the given models to detect the cyberbullying in text by training them with the given dataset which is preprocessed and vectorized with tf-idf

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ChaitanyaC22/Cross_Platform_Product_Mapping_Algorithm_for_Products

This repository contains a product ID mapping solution using TF-IDF vectorizer for weighted text vectors, Facebook AI Similarity Search (FAISS) for coarse filtering with cosine similarity, and Levenshtein distance for refined matching against the Blinkit catalog. Achieved 11.45% match for Zepto and 11.48% for Instamart.

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kodiks/turkish-news-classification

Turkish News Category Classification Tutorial

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nivesayee/recipe-genie

Recipe Genie is a recipe recommendation system that recommends recipes to users based on the ingredients they have at home.

Language: Python - Size: 30.4 MB - Last synced at: about 1 month ago - Pushed at: 10 months ago - Stars: 6 - Forks: 2

yash1th-yerra/Simple-Search-Engine-TfIdf

A Flask-based Search Engine that allows users to search for songs using lyrics snippets! This project demonstrates how to implement a basic text search functionality with TF-IDF Vectorization and Cosine Similarity for ranking results.

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opennlp/Large-Scale-Text-Classification

Large Scale benchmarking of state of the art text vectorizers

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sayande01/Fake_News_Detection_LogisticRegression

This project detects fake news using Logistic Regression with NLP techniques, including NLTK stopword removal, Porter Stemmer for text normalization, and TF-IDF vectorization for feature extraction. It achieves high accuracy and precision, offering a reliable solution to combat misinformation.

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prneidhardt/Natural-Language-Processing

Twitter US Airline Sentiment

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nirmaldeepponnada/CodeClauseInternshipProject1

This project involves Customer Segmentation using K-Means clustering to group customers based on Recency, Frequency, and Monetary (RFM) analysis from the Online Retail dataset. It also performs Sentiment Analysis on Amazon Product Reviews using Natural Language Processing techniques & Logistic Regression to classify reviews as positive or negative.

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BrunoRovea/relevant-event-detection-ml

Language: Python - Size: 19.4 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 1 - Forks: 0

Dutta-SD/AggDetectApp

A web application that detects aggression and misogyny in text using BERT augmentation, sentiment analysis, XGBoost, TF-IDF vectorization, LIME explainability. [Paper accepted at ICON 2021]

Language: Python - Size: 597 KB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 3 - Forks: 0

Nukaze/spotify-recommendations

Implements a content-based recommendation system for Spotify using TF-IDF (Term Frequency-Inverse Document Frequency) and Cosine Similarity. The system analyzes song features to recommend similar tracks based on user preferences.

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coderjolly/news-recommender

This is a news recommender system that uses beautiful-soup to scrape news articles, their categories and descriptions to create a data dump. It then uses word embedding techniques such tf-idf, word2vec for content based news recommendation and LightRF, LightFM to explore hybrid and collaborative filtering based recommender models.

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jalajthanaki/medical_notes_extractive_summarization

Extractive summarizationof medical transcriptions

Language: Python - Size: 12.7 KB - Last synced at: about 1 month ago - Pushed at: about 7 years ago - Stars: 8 - Forks: 9

alaazamelDev/text-based-search-engine

Implementation of a search engine using TF-IDF and Word Embedding-based vectorization techniques for efficient document retrieval

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razamehar/Sentiment-Analysis-using-Deep-Learning---Machine-Learning

Sentiment analysis on the IMDB dataset using Bag of Words models (Unigram, Bigram, Trigram, Bigram with TF-IDF) and Sequence to Sequence models (one-hot vectors, word embeddings, pretrained embeddings like GloVe, and transformers with positional embeddings).

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codeasarjun/Rewordify

You will get other sentences for provided inputs

Language: Python - Size: 405 KB - Last synced at: about 2 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

codeasarjun/MovieMingle

Movie Recommendation System is a web application designed to provide personalized movie recommendations to users based on their input movie titles.

Language: Python - Size: 75.1 MB - Last synced at: about 2 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

steveee27/Multiclass-Text-Classification-of-Presidential-Campaign-Tweets

Explore the Indonesian presidential campaign of 2024 through advanced text classification. This project transforms tweets into insights on national resilience using cutting-edge machine learning models and text preprocessing techniques. Dive into the intersection of politics and data science!

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akthammomani/AI_Powered_Recipe_Recommender

Build a Web App called AI-Powered Recipe Recommender App

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urs-truly-siva/Infosys_Text_Summarizer

TextSummarizer is an AI-powered tool designed to generate concise summaries from longer pieces of text. Leveraging the power of Natural Language Processing (NLP) and machine learning techniques, this tool helps in extracting the most relevant information, making it easier to understand and digest large amounts of data quickly.

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aarryasutar/Hate_Speech_Detection

This project aims to detect hate speech on Twitter using advanced NLP and machine learning techniques, exploring feature extraction methods like TF-IDF and sentiment analysis, and evaluating models such as Logistic Regression and SVM.

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Davityak03/NLP-Bag-of-Words-and-TF-IDF

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2003HARSH/Sms-Spam-Classifier

ML model for spam detection using Naive Bayes & TF-IDF. Achieved 0.98 accuracy. Utilized Scikit-learn, Numpy, nltk. Implements NLP concepts. Explore precise spam classification effortlessly. #MachineLearning #SpamDetection 🚀✉️📱

Language: Python - Size: 119 KB - Last synced at: 2 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

Roaajadaa/content-based-recommender-system

Build a content-based recommender system that suggests items to users based on their preferences (favorite products)

Language: Python - Size: 2.93 KB - Last synced at: 2 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

LasithaAmarasinghe/Movie-Recommender-System

This ML model recommends movies that may align with the user's preferences based on TF-IDF matrix.

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tan264/InformationRestriction

Language: Python - Size: 3.16 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 1 - Forks: 0

vlada-pv/Prediction-Sociolinguistic-Data-Based-on-the-Diaries-Texts-of-the-Prozhito-Project

The repository contains notebooks created for collecting and preprocessing the corpus of diary entries and for experiments on creating models for predicting gender, age groups of authors and the time period of text creation.

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adelelwan24/Arabic-Dialect-Classification

Many countries speak Arabic; however, each country has its own dialect, the aim of this project is to build a model that predicts the dialect given the text.

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HaleyEgan/Exploring-Philosophical-Texts-Across-Gender-and-Time-with-Text-Analytics

Using text analytics to understand cultural patterns in philosophical texts. Exploring gender, author, region, and time-period differences, and extracting key philosophical concepts.

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vn33/Intensity-Analysis-EmotionClassification

Predict emotions (happiness, anger, sadness) from WhatsApp chat data using machine learning and deep learning models. Includes text normalization, vectorization (TF-IDF, BoW, Word2Vec, GloVe), and model evaluation.

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Bill-Klay/Skincare-Recommendation-Android-Application

Skincare recommendation android application that uses dataset from Kaggle and scrapped data from cosmetics websites to work a Tf-IDF vectorizer for content based filtering, and KNN and Decision trees for collaborative based filtering. The notebook also contains other approaches for POC including SVD. Backend APIs are based on Flask, Android application is made using Java with Android Studio whereas Firebase acts as the database and the middleware for relaying login information as well to serve the data to the application.

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jeroenvansweeveldt/Predicting_superhero_creators-machine_learning_exam_2023

Data and code for the machine learning exam assignment of MA Digital Text Analysis (2023).

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Sanjeev-Kumar78/Book-Recommendation-System

This is a book recommendation system based on the book rating data from GoodReads_100k dataset. The dataset contains 100k book.

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vishant-mehta/Music-Recommendation-System-using-Cosine-Similarity

The recommender framework goes about as a friend in need and channels the melodies that are reasonable for that client at that point. It likewise expands the client's fulfilment by playing fitting tune at the correct time, and, in the interim, limit the client's work.

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jeyadosstimothy/ML-on-CrisisLex

Application of Machine Learning Techniques for Text Classification and Topic Modelling on CrisisLexT26 dataset.

Language: Python - Size: 4.41 MB - Last synced at: about 1 year ago - Pushed at: over 6 years ago - Stars: 3 - Forks: 0

AmadejTratnik/money_detect

"Is it about money?" Prompt-powered ML based money sentiment detection.

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Niru8449/Verifacts

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iAmKankan/Natural-Language-Processing-NLP-Tutorial

NLP tutorials and guidelines to learn efficiently

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smartinternz02/SI-GuidedProject-4838-1627119859

In this project, we have built a classifier model using NLP that can identify news as real or fake. For this purpose, we have used data from Kaggle.

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chunwangpro/textual-information-extraction-and-numeric-processing

Extract textual information from Amazon products reviews and draw correlations through regression and fluctuation analysis.

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eiliaJafari/Movie-Review-Classification-TF_IDF-SVC

Movie Review Classification with TF-IDF Vectorize & SVC given a set of text movie reviews that have been labeled negative or positive

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superingram/The-natural-language-processing-repository

Various projects employing a multitude of natural language processing techniques to generate insight. Tokenization, stemming, a TF_IDF vectorizer, sentiment analysis, latent Dirichlet allocation

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Specathon2k21/Team-Brighter-2021-Fake-Reviews-Detection

Fake reviews detection using SGD Classifier , with an flexible user interface

Language: Python - Size: 11.1 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

Annarhysa/Spam-Classifier

Building a basic spam classifier with Tf-IDF Vectorizer and Naïve Bayes model

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Dhrumil-Zion/Predicting-Password-Strength-Using-NLP

Exploring Natural Language Processing by predicting password strength. Explored the TF-IDF algorithm and implemented it for data preprocessing. Will implement the ML algorithm soon :)

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parvez86/Smart-Recruitment-System

A simple Django-based resume ranker website where recruiters post their jobs and candidates applies for their desired vacancies. The system gets the document similarity between the job description and the candidate resumes, generates similarity scores using the KNN model, and rank or shortlist the candidate resumes.

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nataliakoliou/NLP-Various-Implementations

Implementation & analysis of various NLP techniques in Python: 4 projects on tokenization, text classification, sequence labeling, and more

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opsabarsec/NLP-automatic-tagging-stackoverflow-posts

AI - automatic assignment of tags to stackoverflow threads

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Thenx0009/Spam_Classifier_Project

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.

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samimakhan/Spam-Classification-Project

Spam Classifier project for my end-of-semester project for Intro to AI class. We were a group of four people. I worked on all the Naive Bayes models.

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imane-ayouni/News-feed-classification-using-LSTM

a stacked LSTM to categorize textual news feeds

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GNDEC-Minor-Project-KGJ/book-recommendation-system

A recommendation system for books. Built by following two filtering methods that are Collaborative Filtering and Content Based Filtering. Algorithms used are KNN, Pearson Correlation, and TF-IDF. Every dataset used can be easily found in the data folder of the respository.

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SonalSavaliya/Fake-News-Real-News-classification

Built MultinomialNB, Logistic Regression, Random Forests and LSTM with the TF-IDF vectorizer for fake and real news classification. Also performed K-means unsupervised algorithm with PCA and t-SNE.

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Devbijwe/Password-Strength-Checker-Classifications

This GitHub repository contains a Python script for classifying the strength of passwords using a machine learning model based on Random Forest and TF-IDF vectorization.

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EmamulHossen/Fake_News_Detection

Fake News Detection** is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake.

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agushendra7/twitter-sentiment-analysis-using-inset-and-random-forest

Twitter Sentiment Analysis Using InSet (Indonesia Sentiment Lexicon) and Random Forest Classifier

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agushendra7/twitter-sentiment-analysis-using-vader-and-random-forest

Twitter Sentiment Analysis Using Vader Lexicon and Random Forest Classifier

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DanniRodrJ/Content-Based_Movie_Recommendation_System

Sistema de recomendación de películas basado en contenido. Utilizando TF-IDF y la similitud del coseno. La data fue extraída, transformada y analizada para el entrenamiento del modelo. Disponibilizandolo junto con la data limpia para futuras consultas, a través del despliegue con FastAPI y Render.

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mhmdsab/Spam-Classifier

spam classifier with a dataset of 5000 mail

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chlaudiah/Sentiment-Classification-FD-Reviews

Text Classification for Sentiment Analysis using Female Daily's Reviews Dataset

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shrebox/Information-Retrieval

Compilation of Information Retrieval codes.

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ektoravlonitis/Information-Retrieval-System

Retrieve themes from a user inputted query and semantically connect them to lyrical data from songs.

Language: Python - Size: 3.66 MB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

niteshjindal-7/zomato_swiggy--articles-term-frequency-tf-idf

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Navneet2409/netflix-movies-and-tv-shows-clustering

The goal of this project is to use Netflix data (7787,12) to classify and group movies and shows into specific clusters. We will utilize techniques such as K-means clustering, Agglomerative clustering and content-based recommendation systems to analyze the data and provide personalized suggestions to consumers based on their preferences.

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kendalldyke14/NYTCriticsChoicePrediction

New York Times Movie Critics Choice Designation Prediction

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Izu-33/scotus

This repo illustrates the use of NLP techniques in legal analytics. Herein, contributors used the Supreme Court of The United States facts and their corresponding issue areas to predict the outcome of a case. After training an LSTM neural network, contributors implemented the model in a streamlit app.

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esharma3/myers-briggs-personality-prediction

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/

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Nisarg221B/Recommendation-System

Food Recommendation System based on three model of recommendations

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zackhr/Twitter-Sentiment-Analysis-Covid-19

This work begins by collecting a dataset of tweets with twitter-streaming API regarding COVID-19 and classifying them according to the sentiment that is expressed: positive, negative or neutral

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mahyarsab/StackOverflow_Questions_Quality_Capstone_NLP_Project

The objective of this capstone project is to use Natural Language Processing (NLP) to create a machine-learning model that predicts the quality of questions posted on Stack Overflow, a popular question-and-answer platform for software developers.

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rochitasundar/TwitterSentimentAnalysis-BigDataProject

Scrapped tweets using twitter API (for keyword ‘Netflix’) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard

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