Sentiment Analysis of movie reviews by sklearn's naive bayes and TfIdf word vectorizer.
Scikit-learn vectorizer implementing "A simple but tough-to-beat baseline for sentence embeddings." by Arora, Sanjeev, Yingyu Liang, and Tengyu Ma. (2016)
Automated document merging and extractive summarization of news articles
Training a classifier to differentiate between positive and negative movie review sentences in the "sentence polarity dataset v1.0"
Detect Real or Fake News. To build a model to accurately classify a piece of news as REAL or FAKE. Using sklearn, build a TfidfVectorizer on the provided dataset. Then, initialize a PassiveAggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.
Know-Genius an AI Chatbot who's a General Knowledge Genius!
Detect FAKE news using sklearn
Summarization of text using Extractive Summarization. Developed using Python with Spacy & sklearn.
Notebook for experimenting with chat data
Predict the region of origin of an english-speaking tweet author by analyzing tweet content using machine learning classifier
This project is a rest-api that recommend books for user
The 3rd of 4 NLP Projects - this project clusters a corpus of culinary recipe texts. The cuisine of each recipe is known and each cluster is labeled with the majority cuisine in that cluster. New recipes are then introduced and clustered and labeled with the cuisine of the closest cluster.
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