This project constitutes analyzing tweets using the "Sentiment140" dataset by Stanford. The model uses machine learning algorithms and feature extraction techniques to characterize a tweet as either positive or negative.

The linked jupyter notebooks are as follows:

• First.ipynb - Preparation and cleaning of the dataset

• Second.ipynb - Analysis, visualisation and preparing the data for further visualisation

• Third.ipynb - Zipf Law and visualisation of tweet tokens

• Fourth.ipynb - Split the dataset, took TextBlob sentiment analyzer as the Baseline, feature extraction using CountVectorizer and Logistic Regression based classification model applied on unigrams, bigrams and trigrams

• Fifth.ipynb - Feature extraction using TF-IDF and Logistic Regression based classification model applied on unigrams, bigrams and trigrams and analysed the performance of other classification algorithms like Ridge Classifier, Perceptron, Passive-Agressive Classifier, Stochastic Gradient Descent, LinearSVC, L1 based LinearSVC, KNN, Nearest Centroid, Multinomial NB, Bernoulli NB & Adaboost

• Sixth.ipynb - Implemented Doc2Vec model using Gensim for feature extraction. Used DBOW (Distributed Bag Of Words), DMC (Distributed Memory Concatenated), DMM (Distributed Memory Mean), DBOW + DMC & DBOW + DMM on unigrams

• Seventh.ipynb - Implemented Phrase Modelling using Gensim. Implemented DBOW, DMC, DMM, DBOW + DMC & DBOW + DMM on bigrams and trigrams. Also implemented other classification algorithms on the dataset