Semantic Sentiment Analysis Based on Probabilistic Graphical Models and Recurrent Neural Network

TL;DR

This paper explores combining probabilistic graphical models and recurrent neural networks to enhance sentiment analysis by leveraging semantic features, demonstrating improved classification accuracy across multiple datasets.

Contribution

It introduces a semantic-based approach integrating graphical models and RNNs for sentiment analysis, showing significant performance gains over traditional methods.

Findings

Semantic features improve classification accuracy

Graphical models outperform traditional classifiers

Recurrent neural networks enhance sentiment prediction

Abstract

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.