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Analayzing tweets as positive or negative sentiment with the help of supervised machine learning model
Steps
- Build Vocabulary
- Preprocessing(STOP-Wrods, Handles and Stemming)
- Set of Positive and Negative frequencies
- Feature Extraction
- Logisitic Rgreesion and Naive Bayes Classifier
Naive Machine Translation and LSH
Steps
- Word Embeddings
- transform matrices
- compute the Loss function and gradient loss
- Finding the optimal R with gradient descent algorithm
- LSH and document search
- Create Hash Table
- Approximate KNN