Overview / Usage

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or featuresof the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This research mainly compares deep
learning and other current approaches in sentiment analysis.

Methodology / Approach

Existing research has produced numerous techniques for various tasks of sentiment analysis, which include both supervised and
unsupervised methods. In the supervised setting, early papers used all types of supervised machine learning methods (such
as Support Vector Machines (SVM), Maximum Entropy, Naïve Bayes, ...etc. Unsupervised methods include various methods that exploit sentiment lexicons, grammatical analysis, and syntactic patterns.
As deep learning has emerged as a powerful machine learning technique, and produced state-of-the-art results in many application domains, ranging from computer vision and speech recognition to NLP. Applying deep learning to sentiment
analysis has also become very popular recently. This study focuses on the sentiment analysis research based on deep learning.

Technologies Used

Python using Jupyter notebook, Numpy, scikit-learn, data visualization with matplotlib,