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NLP with Classification and Vector Spaces

Translation as linear transformation of embeddings

In this project, I use linear transformation of word embeddings to translate English words to French.

  • Generate embedding matrices from a python dictionary
  • Compute the gradient of loss function(square Frobenius) norm in respect to transform matrix R
  • Find the optimal R with gradient descent with a fixed number of iterations
  • Search for the translation embedding by K-NN(1-NN)
  • Test our translation and compute its accuracy.

Finding the most similar tweets with LSH(locality sensitive hashing)

In this project, I use LSH instead of normal KNN to find the most similar tweet to a specific tweet.

  • Create document(tweet) embeddings
  • Calculate hash number for a vector
  • Implement hash buckets
  • Create hash tables

Logistic Regression Classifier-Sentiment Analysis

in this project, I use logistic Regression for sentiment analysis on tweets(nltk: twitter_samples). Given a tweet, will decided if it has a positive sentiment or a negative one.

  • Process tweets: eliminate handles and URLs, tokenize the tweet into individual words, remove stop words and apply stemming.
  • Extract features for logistic regression
  • Implement logistic regression from scratch
  • Apply logistic regression on a natural language processing task
  • Test logistic regression and calculate the accuracy
  • Predict on your own tweet

Naive Bayes Classifier-Sentiment Analysis

in this project, I use Naive Bayes for sentiment analysis on tweets(nltk: twitter_samples). Given a tweet, will decided if it has a positive sentiment or a negative one.

  • Process tweets: eliminate handles and URLs, tokenize the tweet into individual words, remove stop words and apply stemming.
  • Calculate logPrior and loglikelihood for each word
  • Train a Naive Bayes model on a sentiment analysis task
  • Test Naive Bayes and and calculate the accuracy
  • Predict on your own tweet