John Tsiligaridis, Heritage University, USA

Sentiment analysis (SA) is a Natural Language Processing (NLP) method that helps identify the emotions in text. It is the automated process of identifying and classifying emotions in a text as positive, negative, or neutral sentiment. This way, companies can understand customers’ sentiments, improve their products and services accordingly, and determine effective strategies. The need to discover the algorithm with the best classification performance is obvious. To this end, two different approaches for Sentiment Analysis problems are presented. The first one is based on Machine Learning (ML) models and the second one on Deep Learning (DP) models. Most ML models are flexible depending on their classifier hyperparameters and provide competitive accuracy levels but not all of them. Logistic Regression (LR), Random Forests (RF) of ML and the various models based on Neural Networks (NNs) of DL are applied. Useful results are obtained. Measures for classifiers’ effectiveness are also provided.

Random Forest, Machine Learning, Deep Learning