Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey

Understand

The purpose of the study is to investigate the relative effectiveness of four different sentiment analysis techniques: (1) unsupervised lexicon-based model using Sent WordNet; (2) traditional supervised machine learning model using logistic regression; (3) supervised deep learning model using Long Short-Term Memory (LSTM); and, (4) advanced supervised deep learning models using Bidirectional Encoder Representations from Transformers (BERT).

• We use publicly available labeled corpora of 50,000 movie reviews originally posted on internet movie database (IMDB) for analysis using Sent WordNet lexicon, logistic regression, LSTM, and BERT.
• The first three models were run on CPU based system whereas BERT was run on GPU based system.
• The sentiment classification performance was evaluated based on accuracy, precision, recall, and F1 score.