Performing sentiment analysis on dataset of IMdB's movie reviews by implementing Google's word2vec algorithm

Google's Word2Vec is a deep-learning inspired method that focuses on the meaning of words. Word2Vec attempts to understand meaning and semantic relationships among words. It works in a way that is similar to deep approaches, such as recurrent neural nets or deep neural nets, but is computationally more efficient.

Libraries used in this implementation (Packages installed using pip):

• pandas
• numpy
• scipy
• scikit-learn
• Beautiful Soup
• NLTK
• Cython
• gensim

Classifiers used:

• RandomForest Classifier
• SGD Classifier
• Logistic Regression