Fake news detection
-
Updated
Mar 3, 2021 - Python
Fake news detection
Built using Python, Streamlit, and NLTK, the Hate Speech Detection App employs a Decision Tree Classifier for identifying hate speech in text. It features real-time speech input, NLP preprocessing, and a user-friendly Streamlit interface, offering both visual and text-to-speech result presentation.
Market trends and investment insights
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
This work proposes a new approach for detecting fake news in datasets using some of the most widely used algorithms of machine learning.
This repository houses 3 different Jupyter Notebooks that each analyze the similarity in data points to most effectively inform customer recommendations in the retail space.
Case Study- Text Mining
Case Study- Text MIning
Content-based Recommender System with Natural Language Processing using TF-IDF Vectorizer, Count Vectorizer and KNN.
Performed Sentiment Analysis of Movie reviews using Bag of Words and TF-IDF Vectorizers.
Hope Speech Detection for Equality, Diversity, and Inclusion-EACL 2021
A Google Chrome Extension that estimates the Reliability, Polarity and Subjectivity of any news article on the web. It allows you to like/dislike any article and recommends you articles based on your choices.
Using Multinomial Naive Bayes Classifier to classify SMS messages as SPAM or HAM. Techniques used include Count Vectorizer and Text mining using TF-IDF.
Content-based recommendation engine using Python and Scikitlearn, using concepts of Cosine distance and Euclidean distance. Finally, by using IMDB 5000 movie dataset built a content-based recommendation engine using CountVectorize and Cosine similarity scores between movies.
Some videos have more impact than the others resulting in higher memorability scores for such videos. Using various ML algorithms, such memorability scores are predicted.
Mini NLP-project to build spam-classifier using bag of words technique
Add a description, image, and links to the count-vectorizer topic page so that developers can more easily learn about it.
To associate your repository with the count-vectorizer topic, visit your repo's landing page and select "manage topics."