The study investigates relative effectiveness of four sentiment analysis techniques: (1) unsupervised lexicon-based model using SentiWordNet, (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 model using Bidirectional Encoder Representations from Transformers (BERT). Publicly available labeled corpora of 50,000 movie reviews originally posted on Internet movie database (IMDB) were analyzed. Sentiment classification performance was calibrated on accuracy, precision, recall, and F1 score. The study puts forth two key insights: (1) relative efficacy of four sentiment analysis algorithms and (2) undisputed superiority of pre-trained advanced supervised deep learning algorithm BERT in sentiment classification from text. The study is of value to analytics professionals and academicians working on text analysis as it offers critical insight regarding sentiment classification performance of key algorithms, including the recently developed BERT.

该研究调查了四种情感​​分析技术的相对有效性：（1）使用SentiWordNet的无监督词典模型;（2）使用Logistic回归的传统有监督机器学习模型;（3）使用长短期记忆（LSTM）的有监督深度学习模型，以及（4）使用来自变压器（BERT）的双向编码器表示的高级监督式深度学习模型。分析了最初发布在互联网电影数据库（IMDB）上的50,000个电影评论的可公开标记的语料库。情感分类的表现是根据准确性，准确性，召回率和F1分数进行校准的。该研究提出了两个关键见解：（1）四种情感分析算法的相对功效，以及（2）预训练的高级监督深度学习算法BERT在文本情感分类中的无可争议的优势。这项研究对从事文本分析的分析专业人士和院士具有重要意义，因为它可以提供有关关键算法（包括最近开发的BERT）的情感分类性能的重要见解。