  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.
The study puts forth two key insights: (1) relative efficacy of four highly
advanced and widely used sentiment analysis techniques; (2) undisputed
superiority of pre-trained advanced supervised deep learning BERT model in
sentiment analysis from text data. This study provides professionals in
analytics industry and academicians working on text analysis key insight
regarding comparative classification performance evaluation of key sentiment
analysis techniques, including the recently developed BERT. This is the first
research endeavor to compare the advanced pre-trained supervised deep learning
model of BERT vis-\`a-vis other sentiment analysis models of LSTM, logistic
regression, and Sent WordNet.
