1 research outputs found
Semantic Sentiment Analysis Based on Probabilistic Graphical Models and Recurrent Neural Network
Sentiment Analysis is the task of classifying documents based on the
sentiments expressed in textual form, this can be achieved by using lexical and
semantic methods. The purpose of this study is to investigate the use of
semantics to perform sentiment analysis based on probabilistic graphical models
and recurrent neural networks. In the empirical evaluation, the classification
performance of the graphical models was compared with some traditional machine
learning classifiers and a recurrent neural network. The datasets used for the
experiments were IMDB movie reviews, Amazon Consumer Product reviews, and
Twitter Review datasets. After this empirical study, we conclude that the
inclusion of semantics for sentiment analysis tasks can greatly improve the
performance of a classifier, as the semantic feature extraction methods reduce
uncertainties in classification resulting in more accurate predictions.Comment: 74 pages, Thesi