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