Traditional ML with SIFT & Bag-of-Words vs. Deep Learning with CNN (VGG16 transfer learning). Explore, train, and compare techniques on a diverse face dataset. Ideal for learning image classification

The Movie-Review-Predictor uses a bag of words model to analyze and predict the sentiment of movie reviews.

This Python module can be used to obtain antonyms, synonyms, hypernyms, hyponyms, homophones and definitions.

A content based movie recommender that recommend movies based on tags

🍊 📄 Text Mining add-on for Orange3

The projects here demonstrate how a textual corpus is prepared for analysis, preprocessing steps for computational text mining and extraction of business insights. Concepts such as feature representation using bag of words and TF-IDF are demonstrated, clustering and supervised machine learning algorithms like regression and others are used on a DTM

Project building ML & DL models to detect spam messages.

The ROS package of DBow3

Content: NLP introduction, Components of NLP, Steps to build NLP pipeline, Bag of Words (BoW) model, Term Frequency Inverse Document Frequency (TFIDF) model

Deep learning for natural language processing

The goal of this project is to develop a machine learning model that can classify movie reviews as positive or negative based on the sentiment expressed in the text.

The author implemented logistic regression and support vector machine for topic labelling and applied two feature extractions, Bag-of-Words (CountVectorizer) and TF-IDF (TfidfVectorizer), after which the results for both methods were analyzed.

The author implemented support vector machine for sentiments analysis and applied two feature extractions, Bag-of-Words (CountVectorizer) and TF-IDF (TfidfVectorizer), after which the results for both methods were analysed. The accuracy obtained for both methods were (BoW = 87%) and (TF-IDF = 86%).

Feasibility of a classification engine of articles into different predefined categories, with a sufficient level of precision, based on an image and a description.

Movie Recommender System leverages a content-based approach, suggesting films to users based on the attributes of movies they have previously enjoyed. By analyzing movie metadata such as genre, cast, director, keywords, etc., this project offers personalized recommendations aligned with users' cinematic tastes.

Applies probability based bag-of-words model for toxicity classification of social media texts

Traversing the landscape of computer vision, from foundational image processing to advanced deep learning architectures

Twitter sentiment analysis is the process of analyzing tweets posted on the Twitter platform to determine the overall sentiment expressed within them. It involves using natural language processing (NLP) and machine learning techniques to classify tweets.

The course "Natural Language Processing Applications" in the Artificial Intelligence program at the National Polytechnic Institute (IPN).

Content-based recommendation system that provides viewers with five choices for related movies based on the Cosine Similarity Metrics and the Bag of Words concept.