A fast, robust Python library to check for offensive language in strings.

This is a Natural Language Processing Project using VADER, Afinn, Textblob, Transformers and ML model. Also using Flask.

Machine Learning | Deep 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).

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.

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

In this project, we aim to analyze hotel reviews to determine the underlying sentiment expressed by customers. Our goal is to differentiate between positive and negative reviews using Natural Language Processing (NLP) techniques and machine learning algorithms.

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

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

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.

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 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%).