Deep learning for natural language processing
-
Updated
Jul 7, 2024 - Python
Deep learning for natural language processing
🍊 📄 Text Mining add-on for Orange3
Implementations in Python for COMP425: Computer Vision. This repository covers edge and corner detection, image segmentation and clustering, ...
BoW model in image classification
A fast, robust Python library to check for offensive language in strings.
This Python module can be used to obtain antonyms, synonyms, hypernyms, hyponyms, homophones and definitions.
Applies probability based bag-of-words model for toxicity classification of social media texts
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.
An interactive chatbot that engages with users in real-time conversations, providing personalized responses. With its advanced Natural Language Processing (NLP) capabilities, it can interpret user queries accurately. Created for websites catering to a small niche.
A collaborative project on text mining undertaken as part of the post-graduate program.
ChatBot utilizing neural networks, NLP techniques, and the Bag of Words model. Implements tokenization and stemming for efficient language processing.
Sentiment Analysis on Youtube Video comments
Artificial Conversational Entity for queries in Eulogio "Amang" Rodriguez Institute of Science and Technology (EARIST)
Machine Learning Spam Filter from scratch
Image classifiers that utilizes the Bag of Visual Words (BoVW) approach with three different classification algorithms implemented from scratch: naive bayes, logistic regression, and a neural network.
📑 SeSG (Search String Generator): A approach that uses text mining to build search strings for secondary studies.
A Naive Bayes classifier to detect clickbait headlines
multi label and multi instance learning on DeliciousMIL dataset
Spam detection using Naive Bayes Models
In this research project we used a shift-invariant k-means algorithm to learn a preictal and interictal codebook of prototypical waveforms that can be used to summarize the occurrence of recurrent waveforms and to classify between preictal and interictal segments. We use the common spatial patterns (CSP) method to spatially filter the multichann…
Add a description, image, and links to the bag-of-words topic page so that developers can more easily learn about it.
To associate your repository with the bag-of-words topic, visit your repo's landing page and select "manage topics."